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Semantic Web -1 (2022) 1–59 1
DOI 10.3233/SW-223201
IOS Press
A systematic overview of data federation
systems
Zhenzhen Gu a, Francesco Corcoglioniti a, Davide Lanti a, Alessandro Mosca a, Guohui Xiao b,c,d,*,
Jing Xiong aand Diego Calvanese a,d,e
aKRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy
E-mails: zhenzhen.gu@unibz.it,francesco.corcoglioniti@unibz.it,davide.lanti@unibz.it,
alessandro.mosca@unibz.it,jing.xiong@unibz.it,diego.calvanese@unibz.it
bDepartment of Information Science and Media Studies, University of Bergen, Norway
E-mail: guohui.xiao@uib.no
cDepartment of Informatics, University of Oslo, Norway
E-mail: guohuix@ifi.uio.no
dOntopic S.r.l, Italy
E-mail: guohui.xiao@ontopic.ai
eDepartment of Computing Science, Umeå University, Sweden
E-mail: diego.calvanese@umu.se
Editor: Aidan Hogan, Universidad de Chile, Chile
Solicited reviews: Andriy Nikolov, AstraZeneca, England; Aidan Hogan, Universidad de Chile, Chile; two anonymous reviewers
Abstract. Data federation addresses the problem of uniformly accessing multiple, possibly heterogeneous data sources, by map-
ping them into a unified schema, such as an RDF(S)/OWL ontology or a relational schema, and by supporting the execution of
queries, like SPARQL or SQL queries, over that unified schema. Data explosion in volume and variety has made data federation
increasingly popular in many application domains. Hence, many data federation systems have been developed in industry and
academia, and it has become challenging for users to select suitable systems to achieve their objectives. In order to systematically
analyze and compare these systems, we propose an evaluation framework comprising four dimensions: (i) federation capabili-
ties, i.e., query language, data source, and federation techniques; (ii) data security, i.e., authentication, authorization, auditing,
encryption, and data masking; (iii) interface, i.e., graphical interface, command line interface, and application programming
interface; and (iv) development, i.e., main development language, deployment, commercial support, open source, and release.
Using this framework, we thoroughly studied 51 data federation systems from the Semantic Web and Database communities.
This paper shares the results of our investigation and aims to provide reference material and insights for users, developers and
researchers selecting or further developing data federation systems.
Keywords: Data federation systems, federated query answering, data virtualization, heterogeneous data integration, system
evaluation framework
*Corresponding author. E-mail: guohui.xiao@uib.no.
1570-0844 © 2022 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (CC BY 4.0).
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1. Introduction
The convenience of digitization, the variety of data descriptions, and the discrepancy in personal preferences have
led large enterprises to store massive amounts of data in a variety of formats, ranging from structured relational
databases to unstructured flat files. According to the prediction by Reinsel et al. [1], the global data volume will
reach 163 zettabytes by 2025, and half of that data will be produced by enterprises.
Since data becomes more valuable if enriched and fused with other data, decision-makers need to consider data
distributed in different places and with different formats in order to get valuable insights that support them in their
daily activities. However, data explosion in volume, variety, and velocity – i.e., the “3Vs” of Big Data [2,3]–
increases complexity and makes the traditional ways of data integration [4–6], such as data warehousing [7,8], not
only more costly in terms of time and money but also unable to guarantee the freshness of data. Integration solutions
developed in a more agile way are thus demanded especially in the Big Data context. Data federation is a technology
that makes this possible today, that is becoming more and more appealing in both industry and academia, and that
has been studied for a long time in different communities such as the Database and (more recently) the Semantic
Web ones.
Data federation systems (also known as federated database systems) are traditionally defined as a type of meta-
database management system that transparently maps multiple autonomous database systems into a single fed-
erated database [9,10]. The key task of data federation systems is federated query answering, that is to provide
users with the ability of querying multiple data sources under a uniform interface. Such an interface usually con-
sists of a query language over a unified schema, such as SQL [11] over a relational schema or SPARQL [12] over
an RDF(S)/OWL [13–15] ontology, this interface being often closely related or restricting the query languages
and schemas of supported data sources. Unlike in traditional pipelines for data extraction, transformation, and
loading (ETL) often used in data warehouse systems, federated query answering is achieved by data virtualiza-
tion [16,17], i.e., all the data are kept in situ and accessed via a common semantic layer on the fly, with no data copy,
movement, or transformation. As a result, federated query answering via data virtualization reduces the risk of data
errors caused by data migration and translation, decreases the costs (e.g., time) of data preparation, and guarantees
the freshness of data. Compared to centralized solutions, though, accessing multiple data sources on the fly renders
query answering more challenging [18–20] and requires sophisticated optimization strategies to be devised. Besides
federated query answering, modern data federation systems also offer a wide range of other important capabilities
for data management, such as read-and-write data access for enabling users to both access and modify the data in
the sources, data security for protecting the sensitive data of users and implementing secure data access, and data
governance for managing the availability, usability, and integrity of the data.
Data federation is an active field and many data federation systems have been and are being developed. For exam-
ple, FedX [21,22] and Teiid [23] are two systems supporting respectively SPARQL query answering over multiple
SPARQL endpoints (i.e., standardized HTTP services [24] that can process SPARQL queries) and SQL query an-
swering over multiple heterogeneous data sources, like relational databases, structured files and web services. More
generally, current data federation systems include both industrial systems, mostly developed by software companies
and more mature, and academic systems, mostly developed by research organizations and providing newer func-
tionalities. Moreover, federated query answering facilities are often included in modern data management systems
aimed at heterogeneous big data. These systems include logical data warehouses [25–27], data lakes [28–31], and
polystores [32–36], and can be seen to all intents and purposes as special cases of data federation systems. All the
aforementioned systems present substantial overlap in terms of adopted techniques and extra capabilities offered
to users, while differences in the exposed unified interface may be often bridged – e.g., by using Ontology-Based
Data Access (OBDA) [37] to adapt SQL over a federated relational schema to SPARQL over an OWL ontology –
this way enabling the use of a data federation system in additional scenarios with respect to the ones it was primar-
ily developed for – e.g., use a robust industrial SQL-based data federation system to create a “virtual” knowledge
graph for Linked Open Data publishing. Therefore nowadays, users have access to a large number of data federa-
tion systems to choose among, but selecting the right system for a specific task requires collecting, analyzing, and
comparing the capabilities and techniques of many systems, which is very time-consuming: for industrial systems,
the information needed is usually fragmented and scattered, and the official documents often consist of hundreds
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of pages; for academic systems, conversely, end-user documentation is typically poor or unavailable, and system
features are described in academic publications, when available.
This survey tries to shed some light on this complex matter by analyzing 51 state-of-the-art data federation sys-
tems, jointly covering systems from the Semantic Web and the Database communities thanks to their substantial in-
terchangeability and their commonalities in implemented techniques and features. The considered systems, selected
by following a rigorous and well-founded methodology, comprise 33 industrial systems under active development
and with public official documentation, and 18 academic systems. This work has a twofold goal: help end users in
identifying the systems best suited to their applications and tasks, and allow researchers and developers to gain more
insights into the capabilities, techniques, strengths, and weaknesses of current systems, this way informing further
work in the field.
In order to compare the considered systems from the perspective of data federation in a uniform way, this sur-
vey proposes a qualitative evaluation framework consisting of four dimensions further refined into several sub-
dimensions, which we defined by considering and classifying the aspects that play crucial roles in the users’ choice
of a system for employment in their applications and tasks:
–The federation capabilities dimension concerns the federated query answering features offered by a system
over multiple data sources, both homogeneous and heterogeneous in type. It is further refined into three closely
related sub-dimensions: data source,query language, and federation techniques.
–The data security dimension concerns the capabilities of a system of safeguarding the data in the sources
participating in the federation from unwanted actions by unauthorized users, especially when such data is
sensitive or private. It is refined into five sub-dimensions: authentication,authorization,auditing,encryption,
and data masking.
–The interface dimension concerns the usability of the systems. It is further divided into the three sub-dimensions
of graphical interface,command line interface, and application programming interface, so as to measure the
ability of supporting users in fully appreciating, accessing, and exploiting the features implemented by a sys-
tem.
–The development dimension, finally, concerns the development, release and support practices adopted by sys-
tem vendors. Its five sub-dimensions of main development language,deployment,commercial support,open
source, and release, aim overall at assessing the maturity of the systems and the possibilities for users to get
help from vendors, and to maintain and improve the systems by themselves, if needed.
For all the 51 considered data federation systems, we collect information along the proposed four dimensions by
consulting the official documentation of each system, as well as its related publications. Note that since not all the
features of these systems are properly documented, our analysis is conducted using our best efforts.
This survey adds to an existing body of literature [20,38–43] that reviews the approaches and systems for federated
query answering under multiple perspectives. For example, Oguz et al. [20] evaluate seven SPARQL federation
query engines by focusing on their query evaluation techniques, while Azevedo et al. [42] study the modern data
federation systems (including BigDAWG [33], CloudMdsQL [35], Myria [34], and Apache Drill [44]) by focusing
on their features, owners, goals, and main components. Compared with all these works and summing up, we make
the following contributions:
–We carried out an extensive review of academic literature and documentation about industrial solutions to
identify a large number of data federation systems from the Semantic Web and the Database communities.
–We provide a framework for investigating data federation systems in a uniform and qualitative way by taking
into account aspects of interest for data federation end users, developers and researchers.
–We analyze the identified systems through the proposed framework, this work amounting to an extensive anal-
ysis covering 51 systems and 4 main evaluation dimensions overall divided into 16 sub-dimensions. To the best
of our knowledge, this is the most extensive analysis on data federation so far in terms of investigated systems
and considered aspects.
–As a by-product of our analysis, we make explicit the common capabilities of current data federation systems,
such as the capability of handling heterogeneous data sources, or the query optimization techniques used.
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–We discuss remaining open problems and challenges and point out the research directions that are interesting
and valuable for pursuit.
The remainder of the survey is organized as follows. Section 2presents an outline of data federation. Section 3
illustrates the overall methodology of the survey work. Section 4describes the proposed framework for systems
assessment and comparison. Section 5lists and provides a summary of the selected systems. Section 6thoroughly
analyzes the capabilities of these systems according to the proposed framework. Section 7discusses related work.
Section 8concludes by discussing open problems and challenges as well as giving directions for further work.
Appendices Aand Brespectively provide further details on the specific sources supported by the systems and on
our methodology. A Web version of the tables in this paper, including possible corrections and integrations, is
available on GitHub.1
2. Outline of data federation
This section provides an overview of the main concepts underlying data federation that are addressed in this
paper, for readers not already familiar with them.
The core task of data federation is federated query answering [20,38–41]. For a set of autonomous and possibly
heterogeneous data sources, the goal of federated query answering is to provide a uniform interface, typically as a
unified query language over a unified schema, to access the data of these sources in situ,i.e., without first copying
the data to centralized storage. Given a user query over the unified schema, this task is carried out by issuing and
orchestrating the evaluation of native sub-queries targeting the data sources of the federation.
Figure 1depicts the typical architecture of a federated query engine providing federated query answering. Unified
schema,mappings,metadata catalog are key components, which respectively provide a unified schema of the data
sources participating in the federation, map the data in the sources to the unified schema, and provide statistical
information about the data sources as well as the information of how these data sources can be accessed. For
example, for a relational database, if the unified schema is an RDF ontology, then there exist mappings that map the
tables of this database to the classes and properties of the ontology, and the metadata catalog could list the relevant
content statistics, such as the number of rows of the referred tables, used in federated query optimization. Formally, a
data federation instance usually consists of three components (S,V,M), where Sis a set of data sources S1,...,S
n
which can be relational databases, NoSQL databases, structured files, data warehouses, and so on; Vis the unified
schema for these nsources, such as an RDF(S) ontology or relational schema; and Mis a set of mappings that map
the data of the sources participating in the federation into the elements conforming to the unified schema V. Then
accessing multiple data sources staying in situ simultaneously is carried out by evaluating queries Qexpressed in
terms of the unified schema V(such as SPARQL queries when Vis an RDF ontology, and SQL queries when Vis
a relational schema) via the following steps:
1. Query parsing. This step deals with the syntactic issues of Q,i.e., checking whether the input queries are
syntactically correct w.r.t. the adopted query language(s) as well as the unified schema. Some engines also
transform Qinto an algebraic form, such as a tree structure using internal nodes to denote operations (e.g.,
join, union, or projection) and leaf nodes to denote accessed relations.
2. Source selection and query partition. This step selects suitable data sources for each algebraic component of Q,
and partitions Qinto smaller sub-queries q1,...,q
m(i.e., query chunks) accordingly, based on the mappings
from the data sources to the unified schema V. Approaches for source selection can be index-based, such as
the “triple pattern-wise source selection” for SPARQL queries [45,46], and a way for query partitioning is to
try to “push down” the evaluation of the operators to the data sources, rather than perform such evaluation at
the level of the federation engine.
3. Query optimization & query plan generation. This step computes an execution plan of the partitioned sub-
queries q1,...,q
m, establishing in which order to evaluate the sub-queries and which algorithms to use for
1https://github.com/ontop/ontop-examples/tree/master/swj-2022-federation-survey
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Fig. 1. Typical architecture of a federated query engine (inspired by Oguz et al. [20]).
joining their answers (e.g., bind join, hash join, etc), based on the metadata catalog. Existing approaches may
be rule-based (i.e., via predefined and deterministic heuristic rules) or cost-based (i.e., choose the lowest-cost
execution plan according to some heuristic cost function).
4. Query plan execution. This step, finally, evaluates the decomposed sub-queries q1,...,q
mover the corre-
sponding data sources via the mappings and the metadata catalog, and generates the answers of the original
query Q. Note that, if the query language that the data source supports is different from the query language
of the federation engine, a translation based on the mappings is needed to translate the sub-query into the one
supported by the data source.
Next, we use an example to further clarify the inner workings of federated query answering.
Example 1. Suppose we have a data federation instance ({S1,S
2},V,M)modeling information about a large en-
terprise, as per the one in Fig. 2.HereS1and S2are two data sources storing information about two different
departments. Concretely, S1is a relational database from the Sales department storing the information about prod-
ucts being sold, whereas S2is a NoSQL database from the Human Resources department storing information about
each employee of the enterprise. The unified schema Vof the federation instance is an RDF ontology including the
classes :Product, and :Inspector, as well as the properties :hasCode,:hasInspector,:hasName, and :hasSalary.The
set Mcontains mappings from the data in S1to the terminology :Product,:hasCode, and :hasInspector of V,as
well as the mappings from the data in S2to the terminology :Inspector,:hasName, and :hasSalary.
Suppose we want to retrieve the names of inspected products as well as the names and salary of their relative
inspectors. For this purpose, we formulate a SPARQL query such as Qfrom Fig. 2, consisting of five triple patterns
t1,...,t
5. We send Qto the federation engine for evaluation over the data federation instance. As the first step, the
engine checks the syntax of Qw.r.t. the syntax of SPARQL and the classes and properties declared in V.Afterthe
syntactic check, the engine identifies the sources of each triple pattern in Q, and further partitions Qinto sub-queries
according to some query partition strategy. In our example, by exploiting the mapping set M, the federation engine
selects source S1for triple patterns t1,t2, and t3, and selects source S2for triple patterns t4and t5. Then, by adopting
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Fig. 2. An example of federated query answering.
exclusive groups,i.e., a push down strategy for query partition and optimization [21,22], the engine computes a
partition Q={q1,q
2}of Q, by grouping together the triple patterns corresponding to the same source, so that joins
among them are pushed down to the source and a minimal number of federated joins are evaluated. After that, the
engine computes a plan for evaluating Q. A possible plan is the following: reformulate query q1into a SQL query
q
1and query q2into a NoSQL query q
2, according to the mappings definitions in M; dispatch q
1to S1and q
2to
S2, and evaluate them in a parallel way; merge the returned answers for q
1and q
2to generate the answers of the
initial query Q.
2.1. Transparent vs explicit federated query answering
From the perspective of whether the data source information is transparent for end users, federated query answer-
ing can be classified into transparent federation (the one we have discussed so far) and explicit federation [9,45].
Transparent federation gives users the impression to query one single data source despite data being distributed and
possibly coming from heterogeneous sources [45]. Hence, it is recognized as a general and ideal2solution.
A simplified setting is one where the unified schema is simply a merge of the source schemas, and the user
explicitly states in the query the sources against which it should be evaluated. In such a scenario, we talk about
explicit data federation. This approach is built-in into SPARQL 1.1 through its dedicated SERVICE keyword, and
therefore is supported by any SPARQL-based system fully compliant with SPARQL 1.1, including systems not
primarily focusing on data federation. Figure 3, left-hand side, shows an example of query formulated under the
explicit federation setting, asking for the data from a local RDF store and an explicitly specified remote RDF store.
The right-hand side of the same figure shows the same query formulated under the transparent federation setting,
assuming that foaf:knows and foaf:name are properties belonging to the unified schema.
Compared with transparent federated query answering, the explicit scenario does not require a procedure of source
selection for delivering its task of accessing and joining multiple data sources. However, the burden is placed on end
2https://www.w3.org/2009/sparql/wiki/Feature:BasicFederatedQuery#Feature_description
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Fig. 3. Example of SPARQL query under the explicit federation setting (left-hand side), and its counterpart under the transparent setting (right-
hand side).
users, and this might constitute a major hindrance in case they are not familiar with the data sources participating in
the federation and the data therein contained.
However, the transparent setting is not devoid of drawbacks. For instance, users lose the ability of communicating
with specific data sources directly. Moreover, the transparent situation needs to maintain a unified schema mapped
to multiple data sources, which means that it is more sensitive to schema updates: when the schema of a source is
updated, the unified schema and the mappings may also need to be updated.
2.2. Other capabilities
As mentioned earlier, beyond the core feature of federated query answering, data federation has evolved to offer
a wide range of additional capabilities supporting more powerful and intelligent forms of data consumption and
management. Next, we list some noteworthy capabilities supported by federation systems of this survey.
–Data security. It provides techniques for protecting users’ privacy and sensitive data from leakage. Take the
data federation platform Denodo as an example. The “unified security management” of Denodo offers a single
point to control the access to any piece of information. Different users of Denodo are only allowed to access
either filtered or masked data by using the Denodo role-based security model. Interested readers can refer to
the official documents3for more details;
–Data update. It provides the capability of enabling users to both read and write the data of the sources partic-
ipating in the federation. For example, the SPARQL federation engine FedX4supports SPARQL updates5so
as to make users able to modify the data of the SPARQL endpoints, and the SQL federation engine Denodo
supports SQL data manipulation language (SQL DML) with the motivation of making users able to modify the
data stored in the source databases;
–Data quality. It provides the techniques for guaranteeing the correctness and consistency of data. Take the SAS
Federation Server6as an example. Data quality on SAS Federation Server is implemented through a “SAS
Quality Knowledge Base (QKB)”, allowing for the specification of a set of methods and rules for data quality,
such as rules to cleanse the data.
3https://community.denodo.com/kb/en/view/document/Denodo%20Security%20Overview
4https://rdf4j.org/documentation/programming/federation/
5https://www.w3.org/TR/sparql11-update/
6https://documentation.sas.com/api/docsets/fedsrvag/4.2/content/fedsrvag.pdf
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3. Survey methodology
This survey work stems from our needs for selecting suitable data federation systems for heterogeneous data
integration. Collecting, analyzing, and comparing the existing systems on data federation is a very time-consuming
process. Sharing the results of our study can benefit readers interested in data federation solutions, such as end-
users (consumers), developers, researchers and students. In this section, we present the overall methodology used
for our study. Figure 4provides a snapshot of our methodology, which consists of the identification of the considered
systems,thedesign of the system evaluation framework, and the evaluation of the systems through the framework.
3.1. The methodology for system selection
AsshowninFig.4, the systems considered in our survey are mainly identified through a four stage process:
designing keywords and questions,searching in the search engines,finding the candidate data federation systems,
and filtering according to the inclusion criteria. The bulk of candidate systems collection and filtering required
three months, between the end of 2020 and the beginning of 2021. Although sharing the same stages, the criteria
for selecting academic systems and industrial ones are a bit different. For clarity, in the following, we describe the
selection of academic systems and industrial systems separately.
The selection of academic systems The considered academic systems are selected by reviewing the academic
publications found via surfing the Google Scholar search engine. As a first step, we designed the following keywords
to find the potential systems:
“SPARQL federation”, “SQL federation”, “query federation”, “federated query answering”, “database federa-
tion”, “federated database”, “data federation”, “data virtualization”, “virtual data integration”
Note that for obtaining “more” results, we did not use any operator, like “AND” and “OR”, in the search phrases.
After searching these keywords, we speed-read the titles and abstracts of more than 2000 academic publications
from libraries such as SpringerLink,IEEE Xplore,ACM Digital Library, and so on. By evaluating these titles and
abstracts, we selected and downloaded 295 academic publications for further in-depth reading, consisting of papers,
technical reports, PhD and master theses whose primary focus is on data federation. They include a majority of
system-specific publications and 17 system comparison publications, which range from data federation surveys to
benchmarks, system evaluations and PhD theses reviewing the topic.
After reviewing these publications, we identified a total of 56 academic data federation systems that we narrowed
down to a final selection of 18 representative systems based on the following inclusion criteria:
–Scope. The system must focus on the problem of query federation, or introduce a data federation system.
–Venue. The system must be described in formal publications such as papers in journals or conference proceed-
ings, and not only in preprints or technical reports.
Fig. 4. The overall methodology of the survey work.
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–Availability. The system source code and official website must be available, either linked from the system
publications or findable from the authors’ GitHub profiles (e.g.,theSPLENDID system).
–Relevance. The system must satisfy at least one of the following criteria: it should be mentioned in system
comparison publications; it should provide federation of heterogeneous data sources (e.g., RDBs and CSV
files); it should ensure data security.
–Citations. For period 2020, we do not consider citations. For period 2015–2019, systems should have at least
10 citations. For each prior period 2008, 2009–2011 and 2012–2014, we only consider the system having the
largest number of citations among the ones matching the previous criteria.
The citations criterion aims at keeping the scope of this survey manageable and focused on newer systems, also
considering that most systems earlier than 2015 are covered by other system comparison publications and their
source code is more likely to be unavailable, making them less interesting to our intended audience. Note that to
apply this criterion, a system is classified into the year of its most recent conference or journal publication and a
system number of citations is obtained by summing the Google Scholar citations of all its collected publications as
of 2022/06/07.
Fine details of the selection process are provided in Appendix B, which reports on: (i) the collected 295 academic
publications in terms of aggregated statistics (Section B.1) and full bibliography (Section B.4); (ii) the collected 17
system comparison publications, in terms of metadata, compared systems and considered aspects (Section B.2); and
(iii) the selection of 18 systems out of the 56 identified ones, based on attained inclusion criteria (Section B.3).
The selection of industrial systems To find candidate industrial systems we adopted the Google search engine. We
employed the following generic keywords/questions, aiming at including as many systems as possible:
“data federation”, “data virtualization”, “query federation systems”,
“SPARQL query federation systems/tools/platforms/engines”,
“SQL query federation systems/tools/platforms/engines”,
“data federation systems/tools/platforms/engines”,
“data virtualization systems/tools/platforms/engines”,
“the systems like X”,
where Xdenotes a data federation system already known by us, like Teiid and Denodo. We collected, deduplicated
and reviewed the search results, looking for the websites of industrial data federation systems. Some search results
already corresponded to a system website. Others were instead discussing more broadly about data federation/vir-
tualization or recommending/listing/comparing systems referring to data federation, virtualization or integration, in
which case we browsed page links to identify any referenced system website. As a result, we collected the offi-
cial websites of 72 candidate systems that may provide (due to noise in search results following the use of generic
keywords) the capability of data federation. We then consulted these websites, read the systems descriptions and
documentation carefully, and eventually selected 33 industrial systems for our survey work that strictly meet all the
following inclusion criteria:
–Scope. The system must actually provide the capability of data/query federation.
–Community. There should be evidence for a user community around the system, e.g., via usage statistics and
user messages in fora, mailing lists, issue trackers and the like.
–Documentation. Official system documentation must be publicly available, to support both (perspective) users
and ourselves in conducting the analyses reported in this survey.
–Active development. There must have been at least a system release since 2015/10, i.e., in the last five year
since the time we started this survey (2020/10).
The concrete information of the systems found, as their names, owners, and descriptions, can be found in Section 5.
3.2. The methodology for designing the evaluation framework
To design a framework for evaluating data federation systems in a uniform and qualitative way, also considering
the intended audience of this survey, we focus on answering the following question (see framework design in Fig. 4):
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What aspects of data federation systems are relevant for end users, developers and scholars?
While in principle answers can be obtained by interviewing these three groups (e.g., via questionnaires), this ap-
proach presents two main difficulties: (i) it is hard to identify a representative sample to interview; and (ii) it is hard
for interviewees to answer the question in a general and comprehensive way. Instead, we rely on the fact that data
federation is an established domain that has been studied for decades in both the Database and the Semantic Web
communities, leading to a large body of information from which to extract the aspects of interest that answer our
question. Concretely, we consider three information sources:
–Academic publications. We look for aspects deemed important by other surveys on data federation, or that are
frequent in academic publications referring to data federation systems.
–Official documents. We look for aspects commonly present in official documents of data federation systems,
such as user and developer guides.
–Web pages. We look for aspects that are often considered when comparing data federation systems.7
The system evaluation framework, consisting of four dimensions with sub-dimensions, is generated by combin-
ing, classifying, and further refining the identified aspects. The full process is depicted in Fig. 5. Starting from our
original question (Requirement box), we report the “raw” aspects identified in academic publications, official doc-
uments and web pages (Identification box). They were classified into four categories (Classification box), which
then underwent a series of refining steps (Refinement box), guided both by the information sources we reviewed
Fig. 5. The generation of the system evaluation framework.
7E.g.,https://en.wikipedia.org/wiki/Comparison_of_relational_database_management_systems.
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and by our own expertise as researchers and developers, as well as our own experience on the data federation task
and systems. This refinement results in a final evaluation framework that addressess the information needs of the
different audience groups targeted by this survey:
–End users. They have the concrete need of integrating and federating data sources, and might lack technical
skills like programming. Hence, aspects relevant to them are whether the system is able of handling their data
sources, whether it provides a query language that they are familiar with, whether it offers a graphical interface
to help them to set up a data federation instance easily, whether it provides the services for solving the problems
they may encounter, whether it provides the techniques for protecting their data from leakage, and whether it
is robust enough so as to withstand the technical difficulties that may be encountered in production (e.g., load
spikes, temporary source unavailability, etc).
–Developers. Their need is to work with the systems at a lower-level than end users, for instance through pro-
gramming interfaces, so as to enrich the functionalities delivered by their own applications. Other developers
might also be interested in the source code of the systems themselves, for the purpose of extending it with new
functionalities, e.g., to support more complex data consumption scenarios.
–Researchers and students. They conduct research or studies on data/query federation. Thus, the aspects of
interest for them relate to the knowledge of the capabilities of the systems, or of the strategies they adopt.
All of the aforementioned aspects of interest are captured by the dimensions and sub-dimensions of our evaluation
framework, as it will be detailed in Section 4.
3.3. The methodology of system evaluation
After identifying the considered systems and the evaluation framework, we use such framework to investigate
and analyze the capabilities, strengths, and weakness of the considered systems, e.g., the capability of handling data
heterogeneity. Finally, we point out some open problems and challenges that might be addressed by further research.
4. The framework for system evaluation and comparison
In this section, we present our framework for analyzing and comparing the selected systems under a user and
application perspective in a uniform and qualitative way. Our framework, shown in the right part of Fig. 5, consists
of four dimensions: federation capabilities,data security,interface, and development. Each dimension is further
characterized by sub-dimensions (16 in total). In the remainder of this section we discuss each of these dimensions,
and relative sub-dimensions, in detail.
4.1. Federation capabilities dimension
This dimension evaluates the main task of data federation systems, i.e., federated query answering, in terms of
data source, query language, and federation techniques.
Data source sub-dimension The types of supported data sources usually play a key role when choosing a data
federation system. For example, if a company has massive CSV files that need to be virtually integrated with data
stored in MySQL, then it will preferably take into consideration systems supporting CSV files and MySQL at the
same time. This sub-dimension also permits users to distinguish whether a system focuses on heterogeneous or
homogeneous data sources. Roughly speaking, the more different types of data sources a system supports, the more
capable that system is in accessing heterogeneous data. By reviewing the data sources supported by the considered
systems, we design six types of data sources, like relational and graph-based, to inspect this sub-dimension. The
concrete information will be introduced in Section 6.1.
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Query language sub-dimension We consider the query language(s) provided to users for accessing and managing
the data in the federated sources. Generally speaking, a federation system should preferably adopt a standard query
language that is familiar to most people, like SPARQL or SQL. In this way, users do not need to learn a new query
language when using the system, and existing tools and resources for the adopted language can be reused. We
considered the systems developed within the Semantic Web and Database communities, but not limited to these two
kinds. Hence, we characterize this sub-dimension into SPARQL,SQL, and Other.
Federation technique sub-dimension We refer to the typical architecture for federated query answering described
in Section 2, and assess the main techniques adopted by a system. We focus on the techniques for metadata catalog,
unified schema and mappings,source selection and query partition,query optimization and plan generation, and
query execution. The motivation is to help readers in forming a general idea about the techniques employed by each
system.
4.2. Data security dimension
As a data-centric application, data federation offers a single logical point to integrate data from multiple sources
that may contain sensitive and private data (e.g., financial transactions, users’ contact information, or medical proce-
dures). The protection of such data represents a crucial problem for obtaining the trust of users and data providers.
This problem is further complicated by the risk of leaking sensitive information through analysis and correlation of
otherwise non-sensitive data from separate sources. Therefore, the data security dimension considers whether a data
federation system has the ability of safeguarding data from unwanted actions of unauthorized users, and it is further
organized in sub-dimensions according to the system’s support for the most common data security mechanisms.
Authentication sub-dimension Authentication refers specifically to accurately identifying users before they have
access to data. It is the act of validating that users are whom they claim to be, and is the first step in any data security
process. The most common authentication mechanism is a username and password combination. Other common
authentication mechanisms use shared keys, PIN numbers, or security tokens.
Authorization sub-dimension Authorization is a mechanism for granting or denying access to a resource based on
identity. More generally, it consists in defining an access policy, and is usually implemented through a set of declar-
ative security roles which can be associated to users. Authorization is different from authentication, and usually
happens after authentication.
Auditing sub-dimension Data auditing logs and reports on events like users’ accesses, modifications, changes of
ownership, or permissions regarding sensitive data. Audit procedures increase visibility on data operations and are
instrumental to the investigation and prevention of data breaches and other data security incidents.
Encryption sub-dimension Data encryption algorithms transform the original data into an unreadable format so
that only authorized users having the corresponding key can decrypt and read the information. Encryption is com-
monly employed on data transiting between the system and the user, and possibly on data stored, cached, or other-
wise materialized within the system as well, to protect them from unauthorized low level accesses.
Data masking sub-dimension Data masking8is the process of masking (i.e., obscuring, deleting, or otherwise
scrambling) specific pieces of accessed data, so as to ensure that sensitive information is not exposed to unauthorized
parties (e.g., users, developers, system administrators). Data masking may use lossless techniques such as encryption
or tokenization9that allow retrieving the original unmasked value if the required information is available (e.g.,the
decryption key), but this feature is not a requirement and is not provided by many masking approaches that just
aim at hiding sensitive data (e.g., the simple replacement of data with random values, or with ‘∗’ characters). Also,
differently from encryption that may operate on the whole communication channel between the system and the user,
data masking typically operate on the individual pieces of sensitive data (e.g., a table column or row field).
8https://en.wikipedia.org/wiki/Data_masking
9https://en.wikipedia.org/wiki/Tokenization_(data_security)
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4.3. Interface dimension
The ultimate goal of system development is to support users in fully appreciating, accessing, and exploiting the
features implemented by the system. Its achievement largely depends on the interface(s) offered to users for inter-
acting with the system, which ultimately determine the ease of use, i.e., the usability, of a system. Such interfaces
are the subject of this dimension, whose sub-dimensions are organized according to the different types of interfaces
commonly offered by systems.
Graphical interface sub-dimension Setting up a data federation system is typically a complex task involving an
extensive amount of configuration, e.g., for connecting the federated data sources, acquiring their necessary meta-
data, and setting up the system components. For example, Teiid supports the use of a complex XML configuration
file10 to define a federated database, there called a Virtual Database (VDB). Without fully understanding the syntax
and components of this file, building a VDB is hard for users, especially for the less-technical ones. A graphical
user interface may greatly ease the configuration process, as well as other administration and operation tasks, and
thus largely affects the user friendliness of a system.
Command line interface sub-dimension Data federation systems are typically used as components of larger in-
formation systems, where they need to be integrated with other components, such as business intelligence (BI),
customized dashboards, or machine learning tools, to support or handle much more complex applications and tasks.
To that respect, a command line interface provides a first, simple solution for automatically invoking the function-
alities of a data federation system in other programs or scripts of a larger information system.
Application programming interface sub-dimension A further, more flexible integration mechanism is represented
by application programming interfaces (APIs) offered by the data federation system, such as web APIs or client
libraries in various programming languages (e.g., ODBC/JDBC drivers). Such APIs make it easier for developers to
connect, configure, and operate an instance of the system at run-time within other applications.
4.4. Development dimension
This dimension considers the development, release, and support practices of a system, with its sub-dimensions
capturing the aspects that are most relevant when matching the non-functional requirements of a user (in terms of,
e.g., performance, robustness, flexibility, sustainability).
Main development language sub-dimension The main programming language(s) used to develop the core func-
tionalities of a system influence system requirements (e.g., a Java Runtime Environment is required for the Java
language), performance, customization, and integration options (e.g., embedding the system as a library), and con-
sequently affect the system’s fitness for use in an intended user application.
Deployment sub-dimension The hardware/software infrastructure required to run a system, as well as its economic
viability, are influenced by the deployment options offered for the system. At one end of the spectrum, we have on-
premises deployment where the user obtains the software, possibly for a one-time license fee, and is in charge
of its deployment, maintenance (e.g., updates) and configuration, which may occur on any machine(s) under the
user control (i.e., “on the premises” of the user). The other end of the spectrum is represented by Software as a Ser-
vice (SaaS),11 whereby the system is offered as a pre-deployed service maintained by the provider, and the user only
cares about configuring and using the service on a subscription basis, where costs may depend on “how long” (e.g.,
hours) and “how much” (e.g., number of queries, transfered data) the service is used. In between, Infrastructure as a
Service (IaaS) and Platform as a Service (PaaS) are intermediate options where system deployment and maintenance
are up to the user (as for on-premises deployment), but the system comes bundled with infrastructural resources,
such as virtual machines or middleware, of a cloud provided (e.g., Amazon AWS, Microsoft Azure, Google Cloud
10https://teiid.github.io/teiid-documents/master/content/reference/r_xml-deployment-mode
11https://en.wikipedia.org/wiki/Cloud_computing#Service_models
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Platform), these resources managed to different extents by the user (IaaS) or the provider (PaaS). Examples are con-
tainer platforms like Kubernetes or OpenShift, or cloud marketplaces where virtual machines pre-configured with
the system are obtained and subscription fees are divided among system and infrastructure providers.
Commercial support sub-dimension Learning how to best use an unfamiliar and complex system and dealing with
any issue preventing its normal operation are time-consuming activities, which may result in additional costs or even
in economic losses due to system downtimes. Therefore, the availability of commercial support, e.g., in the form of
training, timely bug fixes, and installation and customization services, plays a keys role when choosing a system.
Open source sub-dimension Systems whose source code is made freely available for modification and redistri-
bution offer users more options for integrating the system while matching specific application requirements, for
improving the system itself, and for maintaining the system even if it is no more supported by authors.
Release sub-dimension We consider the release history and practices of a system, focusing on the number of
releases and the time between the first and the last release of the system. Generally speaking, the longer this time
and the more numerous the releases, the more mature and robust the system typically is, since each new release is
obtained by adding new functions or fixing some issues in the previous one. For example, the first release (v1.0) of
the Denodo platform was in 2002 and the last here considered (v8.0) was in 2020. Thus, Denodo development has
been active for almost 20 years, which makes it potentially more robust than some other younger systems.
5. Overview of the selected data federation systems
Before reporting on the application of the framework of Section 4, we provide here the list and a brief overview of
the selected systems involved in our evaluation and comparison, also to help readers become more familiar with the
current offer on data federation, both industrial and academic, as a whole. For the data federation systems developed
in the context of the Semantic Web community, more academic ones and less industrial ones were found. On the
contrary, for the systems developed within the context of the relational databases community, more industrial ones
and less academic ones were identified.
Table 1lists the selected systems alphabetically, reporting for each one its name with relevant references where to
gather detailed information, academic (name in italics) or industrial nature, provider, and a one sentence description
introducing the system (in its latest version) and complementing the detailed information reported in the next sec-
tions. Note that here and in the following, the information for industrial systems (33 in total) was mainly extracted
from their official websites, while for the academic systems (18 in total), information was mostly extracted and
summarized from their academic publications, although we also considered their online documentation if available.
On the whole, the table exhibits a substantial variability in terms of system provider, nature, and their main
characteristics. Providers range from university and research institutions for academic systems, to open source
organizations, specialized companies, and major corporations for industrial systems. Systems range from database
engines (RDBMS, graph databases, triple stores, polystores, and other multi-model systems) whose storage services
are augmented with data federation capabilities, to purely mediator systems specifically focusing on data federation,
possibly complemented with accessory functionalities (e.g., security). Some industrial systems can be accessed only
as cloud services (SaaS).
6. System evaluation and analysis
In this section, we investigate and analyze in more detail each of the systems overviewed in Section 5, while ap-
plying the four dimensions of the proposed framework. The main goal is to better understand the main characteristics
of each system and to reveal its strengths and weaknesses with respect to the main task of data federation. Notice
that all the systems we investigated have been considered as per their latest version (last update on November 20th,
2021).
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Z. Gu et al. / A systematic overview of data federation systems 15
Tab le 1
Summary of the selected data federation systems. Academic systems in italics
System Provider Description
AllegroGraph [47]Franz Inc. Distributed graph & document DB supporting OWL, SPARQL, SHACL and federation
Amazon Athena [48]Amazon.com, Inc. Inter. cloud query service for Amazon S3 data, based on Presto [49]
Amazon Neptune [50]Amazon.com, Inc. Fully-managed cloud graph DB (property graph, RDF), part of Amazon AWS
AnzoGraph DB [51]Cambridge Semantics Massively-parallel distributed graph DB (property-graph, RDF) for large-scale analytics
Apache Drill [44,52]Apache Software Foundation Distributed schema-free engine for interactive SQL queries on heterogeneous & nested data,
inspired by Dremel [53]
Apache Jena [54]Apache Software Foundation SPARQL query engine of Jena framework and TDB triple store, supporting federation
Apache Spark [55,56]Apache Software Foundation Multi-lang. (incl. SQL) distributed engine for large-scale data processing & analytics
BigDAWG [33,57]Intel Science & Technology
Center for Big data Polystore with heterogeneous storage engines for time series (SciDB), text (Accumulo) and
relational data (PostgreSQL)
Blazegraph [58]Systap Triple store supporting SPARQL 1.1 federation and powering Wikidata (via a fork)
CloudMdsQL [35,59]Inria & LIRMM Polystore integrating heterogeneous storage engines (incl. RDBMS, NoSQL, HDFS)
Comunica [60]Univ. Ghent Modular JS federated query engine for heterogeneous web sources, incl. SPARQL endpoints
CostFed [61]Univ. Leipzig Index-assisted, cost-based data federation system for SPARQL endpoints
DARQ [45]Univ. HU Berlin Earliest data federation system for SPARQL endpoints, cost-based
Data Virtuality [25]Data Virtuality GmbH Heterogeneous data integration solution combining data virtualization and ETL
Denodo [62]Denodo Technologies Inc. Data virtualization solution for heterogeneous sources, also available as cloud service
Dremio [63]Dremio Corporation Data “lakehouse” (lake + warehouse) solution supporting heterogeneous data sources
FEDRA [64]Univ. Nantes (LINA lab.) Data federation system for SPARQL endpoints exploiting data replication
FedX (RDF4J) [21,22]fluid Operations AG On-demand (no statistics, query-time) data federation system for SPARQL endpoints
GraphDB [65]Ontotext Triple store featuring OWL reasoning, SPARQL federated queries & RDBMS access
HiBISCuS [66]Univ. Leipzig Source selection for SPARQL data federation (DARQ, FedX & SPLENDID extension)
IBM Cloud Pak for Data [67]IBM Data federation system with data discovery, governance, security and privacy solutions, also
available as cloud service (formerly IBM Cloud Private for Data)
IBM Db2 Big SQL [68]IBM Massively-parallel Hadoop SQL engine for heterogeneous sources (formerly IBM SQL)
IBM InfoSphere Federation
Server [69]IBM SQL-based data federation system for heterogeneous sources (formerly WebSphere Federation
Server)
JBoss Data
Virtualization[70]Red Hat, Inc. Data federation system based on Teiid and providing read/write access to heterogeneous
sources, data security, and multiple user interfaces / APIs
Metaphactory [71,72]metaphacts GmbH KG platform on top of SPARQL endpoints with two federation engines (Ephedra, FedX)
Myria [34]Univ. Washington Cloud service for big data management/analytics with parallel & federated query engine
Neo4j (Fabric) [73]Neo4j, Inc. Federation solution of Neo4J graph DB (Cypher [74] queries on property graph model)
Obi-Wan[75,76]Inria & Polytechnic Institute
of Paris Ontology-Based Data Access (OBDA) [77] system on top of Tatooine [78] mediator for
heterogeneous sources
Odyssey [79]Univ. Aalborg & Univ. Nantes Statistics & cost-based optimizer for SPARQL data federation (FedX extension)
Ontario [28]L3S Research Center Heuristics-based system using RDF Molecule Templates (introduced by its predecessor
MULDER [80]) to describe/map source content as star-shaped RDF instance descriptions
Onto-KIT [81]Univ. Toulouse Data federation system focusing on Earth Observation data with hypergraph-based data model
and query processing techniques
Oracle Big Data SQL [82]Oracle Corporation Data federation system for Oracle DB that accesses Hadoop storage & processing
Oracle DB (Spatial &
Graph) [83,84]Oracle Corporation Oracle DB component for semantic technologies with data federation capabilities (RDF
views) over relational, graph, and RDF (SPARQL) sources
PolyWeb [32,85]Univ. NUI Galway SPARQL-based data federation system for different sources on the Web (RDF & CSV data,
RDBMS), focusing on source selection, query optimization & execution
Presto [49,86]Presto Foundation SQL-based distributed query engine suitable to interactive (big) data analytics
Querona Data
Virtualization[87]YouNeedIT Sp. z o.o. Sp. k. Data federation system for a variety of heterogeneous sources, based on Apache Spark and
targeting big data analytics with the support of main BI tools
RDFLib [88]RDFLib team A pure Python package for working with RDF, supporting SPARQL 1.1 federation
SAFE [89]Insight SFI Research Centre
for Data Analytics Data federation system for SPARQL endpoints exposing RDF data cubes with sensitive data,
featuring access policy-aware data summaries, source selection & query execution
SAGE [90]Univ. Nantes SPARQL engine with “web preemption” (i.e., query suspend/resume) & federation capabilities
SAP HANA [91]SAP SE In-memory DB targeting with data federation capabilities, also available as cloud service
SAS Federation Server [92]SAS Institute Data federation system featuring data caches, masking, encryption & quality functions
SemaGrow [93]IIT NCSR ‘Demokritos’ Data federation system for SPARQL endpoints with statistics-based query optimization
SPLENDID [46]Univ. Koblenz-Landau Data federation system for SPARQL endpoints that provide VOID [94] data statistics
SQL Server (PolyBase) [95]Microsoft Corporation SQL Server component for data federation supporting Hadoop and Azure storage
Squerall [96]Univ. Bonn Data federation system for heterogeneous sources built on Spark, Presto, and RML mappings
Starburst [97]Starburst Data, Inc. Commercial distribution of Trino, extra security features, available on-premise/on-cloud
Stardog [98]Stardog Union KG platform including data federation of heterogeneous sources & query-time inference
Teiid[23]Red Hat, Inc. SQL-based engine for data federation of heterogeneous sources
TIBCO Data
Virtualization[99]TIBCO Software Inc. Data federation system for heterogeneous sources, with data caching & security, massively
parallel processing & GUI tools (formerly Composite, then Cisco Data Virtualization)
Trino [100]Trino Software Foundation SQL-based query distributed engine for interactive big data analytics, forked from Presto
Virtuoso[101–103]OpenLink Software Multi-model DB (object-relational, RDF, XML) with data federation facilities
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6.1. Federation capabilities dimension
In this subsection, we evaluate the selected systems with a special attention to their capabilities to support fed-
erated query answering. In doing this, we will highlight the query languages that are supported, the data sources
each system is able to manage, and the adopted federation techniques. Concerning the first two aspects, a synthetic
overview of the query languages and the types of data sources supported by the investigated systems is presented
in Table 2. The concrete data source implementations (e.g., MySQL) supported by each system are instead listed in
Table 7of Appendix A.
Query language For columns 2–4 of Table 2, we can make the following observations:
1. With no significant distinction between industrial or academic systems, the standard and popular query lan-
guages SQL and SPARQL are adopted by most of these systems to query the data involved in the federation.
This choice definitely eases the integration of the system with other possible interacting applications. Notice
also that BigDAWG, CloudMds, Myria, and SAS Federation Server use alternative languages inspired by SQL
to support the required capabilities in the distributed federation environment. Instead, Neo4j adopts the declar-
ative graph language Cypher [74] as its underlying query language, with the motivation of making graph data
querying easy to learn, understand, and use by the final users.
2. There exist very few systems that adopt multiple query languages at the same time. Among them, for instance,
AllegroGraph supports SPARQL and Prolog simultaneously; GraphDB provides the capability of processing
SPARQL, SQL, and Cypher queries; and Virtuoso takes both SPARQL and SQL as its query languages. This
situation can be explained by taking into account that (i) the importance or necessity of supporting multiple
query languages is unknown or ignored, and (ii) supporting multiple query languages within the very same
system requires a lot of work from an engineering and development point of view.
3. Some of the academic SPARQL-based systems support only BGP-like queries, such as Obi-Wan [75] and
Squerall [96]. Other systems support general SPARQL queries but their publications only discuss federa-
tion techniques tailored towards BGPs, such as CostFed [61], HiBISCuS [66], Ontario [28], PolyWeb [32],
SAFE [89], SemaGrow [93] and SPLENDID [46]. General SPARQL support may be achieved by relying on a
fully-fledged SPARQL engine like RDF4J12 (formerly Sesame) that supports further operators such as UNION
and OPTIONAL.
4. For systems supporting SPARQL federation, only a few systems, like Amazon Neptune and Apache Jena,
provide the capability of explicit query federation via the SERVICE keyword. Among non-SPARQL systems,
only CloudMdsQL does not support transparent federation.
Data source Uniformly evaluating and analyzing systems in terms of supported data sources is a challenging
task for several reasons. Firstly, system providers usually adopt different standards and granularity to describe the
data sources they support. Some systems classify supported data sources differently and possibly in incompatible
ways. For example, relational sources all go under the databases class in Teiid,13 while Denodo14 distinguishes
between the classes of JDBC databases,ODBC sources, and multidimensional databases. Instead, Apache Drill15
and Trino16 list all the data sources they support without any classification, and IBM Cloud Pak for Data Virtu-
alization17 solely classifies the supported data sources into IBM data sources, third-party data sources, and files.
Secondly, systems may list as supporting both a generic data access interface (e.g., JDBC, ODBC, ADO.NET, OLE
DB, SPARQL HTTP protocol, etc) and some data sources available through that interface, with different meanings.
Often, the listed sources are just examples or special cases for which additional capabilities are implemented, and
12https://rdf4j.org/
13https://teiid.github.io/teiid-documents/master/content/reference/r_data-sources.html
14https://community.denodo.com/docs/html/browse/8.0/en/vdp/vql/generating_wrappers_and_data_sources/creating_data_sources/creating_
data_sources
15https://drill.apache.org/docs/connect-a-data-source-introduction/
16https://trino.io/docs/current/connector.html
17https://www.ibm.com/docs/en/cloud-paks/cp-data/4.5.x?topic=data-supported-sources
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Z. Gu et al. / A systematic overview of data federation systems 17
Tab le 2
Evaluation of query language and data source sub-dimensions. Academic systems in italics. “–” denotes feature/information not found in the
systems’ official documentation, websites, or academic publications, to the best of our efforts
System
Query language Data source
SPARQL SQL Other Relational Graph-
based
Aggregate-
oriented
Structured
Files
Web Service
Paradigms Other
AllegroGraph –Prolog –– – – –
Amazon Athena –– –
Amazon Neptune – – – – – – –
AnzoGraph DB –Cypher – –
Apache Drill –––
Apache Jena – – – – – – –
Apache Spark ––– – – –
BigDAWG – – BigDAWG Query –– –
Blazegraph – – – – – – –
CloudMdsQL – – CloudMdsQL – – –
Comunica –GraphQL ––– –
CostFed – – – – – – –
DARQ – – – – – – –
Data Virtuality ––
Denodo –GraphQL –
Dremio ––– – –
FEDRA – – – – – – –
FedX (RDF4J) – – – – – – –
GraphDB Cypher – – – –
HiBISCuS – – – – – – –
IBM Cloud Pak for Data –––
IBM Db2 Big SQL ––– –
IBM InfoSphere Federation Server ––– –
JBoss Data Virtualization –––
Metaphactory – – ––
Myria –MyriaL – –
Neo4j (Fabric) – – Cypher –– – – –
Obi-Wan – – – – –
Odyssey – – – – – – –
Ontario – – – –
Onto-KIT – – – – – – –
Oracle Big Data SQL ––– –
Oracle DB (Spatial & Graph) – – – – – –
PolyWeb – – –– –
Presto –––– –
Querona Data Virtualization ––– –
RDFLib – – – – – – –
SAFE – – – – – – –
SAGE – – – – – – –
SAP HANA ––– – – –
SAS Federation Server – – FedSQL – – – –
SemaGrow – – – – – – –
SPLENDID – – – – – – –
SQL Server (PolyBase) ––– – –
Squerall – – – – –
Starburst ––– –
Stardog – – –
Teiid –––
TIBCO Data Virtualization –––
Trino –––– –
Virtuoso – – – – –
Number 27 22 10 32 30 24 24 10 20
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18 Z. Gu et al. / A systematic overview of data federation systems
additional sources may be configured (e.g., by tuning the employed SQL dialect) and connected through the inter-
face. In some cases, however, the listed sources are simply the only ones supported through the interface, which we
thus disregard in our assessment. Finally, sources not supported directly by a system, may be supported indirectly
by combining the system with a suitable third-party adapter component, such as a SQL connector exposing a non-
relational data source (e.g., MongoDB) through a standard relational interface (e.g., JDBC), as further discussed in
Appendix A. Since such combinations are potentially limitless and the feasibility of each should be assessed (e.g.,
to verify whether combined components are actually compatible), we here consider only directly supported sources
and further discuss the issue in Section 6.5. Overall, all the aforementioned factors make it difficult to assess the
supported data source sub-dimension uniformly and precisely.
In order to understand the status quo of handling the variety dimension of big data in the data federation setting, af-
ter inspecting the data sources supported by each system, we take the following 6 types of sources into consideration:
(i) Relational, including SQL-based RDBMS, (federated) relational query engines, and distributed/cloud relational
stores; (ii) Graph-based, including SPARQL endpoints, RDF triple stores and property graphs; (iii) Aggregate-
oriented, including key-value stores, wide-column stores, document stores and other NoSQL stores and search
engines that organize data as “aggregates” [104], ranging from opaque values to arbitrarily complex nested docu-
ments;18 (iv) Structured Files such as CSV, JSON and XML; (v) Web Service Paradigms to access arbitrary web
sources, such as HTTP/REST and SOAP/WSDL (vs. specific web APIs like Twitter one); and (vi) Other. We man-
ually classified each occurrence of a specific data source (e.g., MySQL, MongoDB) among the ones supported by
a system, under one of the considered 6 data source types (e.g., relational and aggregate-oriented, respectively), de-
pending on how the specific source is accessed by the system and also relying on established system classifications
(e.g., DB-Engines [105] and Database of Databases [106] catalogs). We use “Other” as a container for all those
infrequently supported sources not covered by the former 5 types, such as directory services, streaming and event
data processing systems, specialized databases (e.g., for time series data) and protocols (e.g., IMAP), and various
specialized web APIs. We remark that source classification is not global across systems but rather local to each
data federation system supporting that source, so for instance a multi-model database like Virtuoso (when used as a
source) may be classified as relational if accessed via SQL, or as graph-based if access occurs via SPARQL.
By combining Table 2and Table 7, we can observe the following:
1. Industrial systems usually support more data sources than academic systems (respectively, 3.2 vs 1.9 distinct
source types per system on average). Consider for example Data Virtuality, which covers all the source types
we considered. It is an unsurprising conclusion, since industrial systems usually focus more on coverage.
2. As for the systems covering multiple, possibly heterogeneous, types of data sources, no matter whether indus-
trial or academic, relational sources have been considered extensively, and most of the mainstream RDBMS
implementations have been supported (cf. second column of Table 7). This may be caused by the dominant
role of relational sources in organizing data. This dominant role, along with the generality and well-understood
semantics of the relational model, might also partially explain the proliferation of SQL connectors/adapters
for non-relational data sources (see discussion in Appendix A). Such proliferation facilitates, for a data fed-
eration system supporting the connector/adapter data access interface (e.g., JDBC), extending the support to
additional, unanticipated data sources.
3. Structured files like JSON, XML, and CSV, because of their importance and wide use, are also directly sup-
ported as native data sources by many systems considered in this survey (24 out of 51, i.e., 47%). Other
systems not directly supporting structured files may instead support the database systems commonly used for
storing and indexing the kind of data of these files (e.g., MongoDB and Elasticsearch for JSON data).
4. Aggregate-oriented sources mostly consist of NoSQL systems (cf. the fourth column of Table 7), exhibit
overall support (24 systems out of 51, i.e., 47%) similar to the one for graph-based sources and structured
files, and are present both in industrial systems (18 out of 33, i.e., 55%) and, to a lesser degree, in academic
systems (6 out of 18, i.e., 33%).
18We use the broad “aggregate-oriented” category due to the difficulty of classifying many NoSQL stores into a single fine-grained category
(e.g., Amazon DynamoDB is independently classified as key-value, wide-column, or document store by different academic and web sources).
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 19
5. Web service paradigms, although important (many sources are available only as web services), are considered
less often (10 systems out of 51, i.e., 20%). This may be caused by the difficulty of implementing federated
query answering over such kind of data, as their data models (where defined) and access patterns (usually
restricted) are very dissimilar from the ones exposed by the data federation system to its users.
6. Other sources in our classification consist mostly of specialized web APIs (cf. last column of Table 7) and are
supported by industrial systems (18 out of 33, i.e., 55%) more than academic systems (2 out of 18, i.e., 11%).
7. Systems supporting SQL queries focus on relational sources (21 systems out of 22, i.e., 95%) while graph-
based sources have rarely been taken into account (5 out of 22, i.e., 23%). Conversely, systems supporting
SPARQL queries focus on graph-based sources (25 systems out of 27, i.e., 93%) but support relational sources
more frequently (10 out of 27, i.e., 37%) than SQL systems do with graph-based sources.
Federation techniques Besides the supported query languages and data sources, we also considered the specific
techniques used by each of the selected systems. Table 3organizes such techniques according to the main compo-
nents of a typical data federation system as shown in Fig. 1. Note that the categories Unified schema and mappings
and Source selection and query partition are only suitable for transparent federation. For each technique, we provide
references to the literature and a list of systems for which the adoption of such technique is stated in official docu-
ments or publications. Hence, the lack of the indication of a particular system under a particular technique has to be
interpreted as unavailable information, and not as negative information. This holds true especially for closed-source
industrial systems, where information about these technical aspects is often covered scarcely or not covered at all in
systems’ documentation. We next discuss each element of Table 3.
–Metadata catalog. A fundamental classification of federation techniques for this component distinguishes be-
tween techniques where the metadata catalog is automatically built out of source metadata accessed in a stan-
dard way (e.g., via the SQL “Information Schema”), and techniques that allow for manual provision of such
metadata by users. These technique families are complementary and a system may adopt one or both of them
(e.g., Denodo, see Table 3for other examples). Manually supplied metadata may be described through self-
defined dialects, such as the XML syntax of Teiid and the RDF molecule template of Ontario. Alternatively,
some systems adopt standard languages, such as the VoID [94] vocabulary for Linked Data [126](e.g., Squer-
all) or the SQL extension for the “Management of External Data”, SQL/MED [127](e.g., Data Virtuality
and Teiid, in alternative to its own XML). SQL/MED provides specialized SQL data definition language (SQL
DDL) statements, such as CREATE FOREIGN TABLE, for defining the objects stored in the federated sources
and how to access them. In place of SQL/MED, other systems (e.g., Apache Spark) use regular or customized
versions of plain SQL DDL statements, such as CREATE TABLE with additional clauses, for the purpose of
acquiring catalog metadata and without the intent of actually modifying the source itself.
–Unified schema and mappings. We divide the federation techniques for this component into two families: the
one where the virtual schema is simply a merge of all the source schemas, and the one where the virtual schema
is fully customizable by the user. In Table 3, many examples of the former category are SPARQL-based systems
that federate SPARQL endpoints, while most of the examples of the latter category are either systems such as
PolyWeb that allow the definition of a flexible virtual schema through R2RML/RML mappings, or SQL-based
systems that allow the definition of views over the source data, as well as constraints over such views (e.g.,
primary and foreign keys).
–Source selection and query partition. A common approach for the identification of the sources of a query
relies on the pre-computation of an index out of the information available in the metadata catalog. Another
technique involves the evaluation of probing queries and is exemplified by many SPARQL-based systems in
Table 3. One of them is FedX (RDF4J), which issues a probing SPARQL ASK query for each triple pattern
in the input query, so as to dynamically identify non-empty sources for that pattern in a more precise, albeit
slower, way than using the index. Some systems, like SPLENDID, combine these two approaches to gather
their respective strengths. Other systems, like HiBISCuS, propose a refinement of the query-based strategy
where the candidate sources identified by the probing queries are further pruned through an analysis based on
the structure of the SPARQL query. For SQL-based systems, source selection is straightforward in the typical
scenario where tables of the unified schema are mapped 1:1 to their respective sources, but becomes non-trivial
when table data is contributed by multiple sources, as it occurs with data partitioning or replication. Teiid
CORRECTED PROOF
20 Z. Gu et al. / A systematic overview of data federation systems
Tab le 3
Summary of the main techniques used in federated query answering, grouped by affected main component of a typical data federation system. For
each technique, we provide references to the literature describing the technique, as well as example systems known to implement the technique
Techniques
Automatic collection of source metadata (e.g., data summaries [32,61])
Example Systems: AnzoGraph DB, Apache Spark, CostFed, Data Virtuality, Denodo, Dremio, FedX(RDF4J), HiBISCuS, IBM Cloud Pak for
Data, JBoss Data Virtualization, Neo4j(Fabric), Odyssey,Ontario, Oracle Big Data SQL, PolyWeb, Presto, SAFE,SAP
HANA, SAS Federation Server, SemaGrow,SPLENDID, Starburst, Stardog, Teiid, TIBCO Data Virtualization, Trino
Manual provision of source metadata (e.g., VoID [94], Sevod [107], Service descriptions [45])
Metadata
catalog
Example Systems: Apache Drill, DARQ, Denodo, JBoss Data Virtualization, Oracle Big Data SQL, Presto, Querona Data Virtualization,
SemaGrow,SPLENDID, Starburst, Teiid, Trino,
Simple merge of schemas
Example Systems: Comunica,CostFed,DARQ,HiBISCuS, Neo4j(Fabric), Odyssey,Ontario,SAFE
Configurable unified schema (e.g., virtual databases [23])
Unified
schema and
mappings Example Systems: AnzoGraph DB, Apache Spark, Data Virtuality, Denodo, Dremio, IBM Cloud Pak for Data, JBoss Data Virtualization,
Onto-kit, Oracle Big Data SQL, PolyWeb, Querona Data Virtualization, SAP HANA, SAS Federation Server, Squerall,
Stardog, Teiid, Trino
Index-based [32,46,66,89,108]
Example Systems: CostFed,DARQ,FEDRA,HiBISCuS,Ontario,PolyWeb,SAFE,SPLENDID
Query-based [22,32,46,66,89]
Example Systems: FEDRA, FedX(RDF4J), HiBISCuS,PolyWeb,SAFE,SPLENDID
Graph-based [61,66,81,89]
Example Systems: CostFed,HiBISCuS,Onto-kit,SAFE
Push down [109, p. 326][110–115]
Source
selection
and query
partition
Example Systems: Data Virtuality, IBM Db2 Big SQL, SQL Server (PolyBase), Starburst, Teiid, Trino
Cost-based optimization [116]
Example Systems: Apache Drill, Apache Spark, CostFed,DARQ, Data Virtuality, Denodo, Dremio, IBM Cloud Pak for Data, Neo4j(Fabric),
Odyssey, Presto, SAP HANA, SemaGrow, SQL Server (PolyBase), Starburst, TIBCO Data Virtualization, Trino
Rule-based optimization [21,28,32,117,118]
Example Systems: Data Virtuality, FedX(RDF4J), Ontario,PolyWeb, SAP HANA, Teiid
Materialization [109, §13.5]
Query
optimization
and query
plan
generation
Example Systems: Denodo, IBM Db2 Big SQL, JBoss Data virtualization, Starburst, Teiid
Bind join [109, p. 166][20,45,93]
Example Systems: CostFed,DARQ, Data Virtuality, FEDRA, FedX(RDF4J), PolyWeb,SAFE,SemaGrow,SPLENDID, Teiid
Nested loop join [20,116]
Example Systems: Apache Drill, DARQ, Data Virtuality, Denodo, FEDRA, FedX(RDF4J), PolyWeb,SAFE, TIBCO Data Virtualization
Hash join [20,116]
Example Systems: Apache Drill, Apache Spark, CostFed, Denodo, SemaGrow,SPLENDID, TIBCO Data Virtualization
Merge join [116]
Example Systems: Apache Drill, Apache Spark, Data Virtuality, Denodo, SemaGrow, TIBCO Data Virtualization
Broadcast join [119–121]
Example Systems: Amazon Athena, Apache Drill, Apache Spark, Starburst, Trino
Partitioned (shuffle) join [122–124]
Example Systems: Apache Spark, BigDAWG, Presto, Trino
Semijion [116]
Example Systems: TIBCO Data Virtualization
Parallelization [109, §8.4][20]
Example Systems: AllegroGraph, Amazon Athena, Apache Drill, Data Virtuality, Denodo, FedX(RDF4J), Squerall, Starburst, TIBCO Data
Virtualization
Data Movement/Ship [125]
Example Systems: Denodo, TIBCO Data Virtualization
Caching [20,22,122]
Query
execution
Example Systems: Apache Drill, Apache Spark, Comunica, Data Virtuality, Denodo, Dremio, FEDRA, FedX(RDF4J), HiBISCuS, IBM Cloud
Pak for Data, IBM Db2 Big SQL, JBoss Data Virtualization, Presto, SAFE, SAP HANA, SAS Federation Server, Teiid,
TIBCO Data Virtualization, DARQ
“multisource models”,19 for instance, support horizontal table partitioning across sources (e.g., an employee
table partitioned across departments) by defining a source-denoting column (e.g., the department name) in the
unified table schema, and exploiting WHERE conditions on that column to select a subset of sources to answer
19https://teiid.github.io/teiid-documents/master/content/reference/r_multisource-models.html
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 21
the query. For both SPARQL- and SQL-based systems, once sources are identified, query partitioning into sub-
queries may involve the push down of query operators to those sources supporting them. An example is the
push down of join operators to RDBMS sources [109, p. 326], a technique pioneered in the Garlic system [110].
–Query optimization and query plan generation. Some systems rely on fully-fledged cost models for generating
an optimized query plan, as per the traditional setting of query answering against a single relational datasource.
This plan also indicates the evaluation order of sub-queries and the types of joins to be used to combine their
results. In other systems, like Ontario, the optimization is purely driven by heuristics and optimization steps are
performed according to a pre-defined set of deterministic rules, such as pushing down certain operators (e.g.,
selection, projection) as much as possible to reduce the size of intermediate results. Cost-based and rule-based
optimization may be also combined to attempt generating better query execution plans, as done for instance
by Data Virtuality and SAP HANA. Finally, a complementary technique is the creation of materialized views,
which can be used in place of re-computing each time the result of expensive distributed operations, in those
scenarios where the source data is expected not to change frequently.
–Query execution. Apart from standard join techniques such as nested loop or hash join, data federation sys-
tems provide techniques for query plan execution that are specifically tailored towards the federated setting.
A common trait of these techniques is that they aim at minimizing data movement across the different sys-
tems participating in the federation. In the bind join between two relations, the outer relation is sequentially
scanned for join values, which are then used to “bind” the attributes in the inner relation. For each such bind,
the matching tuples in the inner relation are transferred to the source of the outer relation and used to construct
the result. This approach can be seen as multiple application of the semijoin technique, where one side of the
join is first filtered with the matching values, and then this “reduced” relation is sent to the other source for
performing the actual join. The broadcast join, instead, “broadcasts” the matching tuples of the inner relation
to all sources in the federation, which is an effective strategy when the outer relation is spread across several
sources and the inner relation is much smaller than the outer relation. Splitting relations into smaller chunks
lies at the basis of the partitioned join, where relations are partitioned according to values of the join keys. This
join technique works in combination with parallelization, where computation is performed in a distributed way
across multiple nodes at the same time. Finally, caching of the intermediate results allows further diminishing
the number of distributed operations performed, and is popular among industrial systems.
6.2. Data security dimension
We evaluate here the data security dimension. The concrete investigation results are shown in Table 4,orga-
nized according to the sub-dimensions of authentication, authorization, auditing, encryption, and data masking. In
particular, by analyzing the information we synthesized in the table, the following can be observed:
1. Almost all the considered industrial systems (31 out of 33, i.e., 94%) provide security mechanisms, such as
authentication and authorization, to protect against unauthorized data access and leaking. This shows that the
importance of data security is actually recognized by system providers in the data federation setting, where
integrating multiple data sources via a unified virtual layer has the potential of making the private and sensitive
data contained in federated sources more likely to be revealed.
2. Among the inspected mechanisms, authentication and authorization are definitely the most frequently adopted
ones (see total counts in Table 4) and are implemented by almost all the industrial systems to identify users
and control their access to data. For example, the Denodo Platform supports role-based authentication20 and
enforces strict and fine-grained row and column level access control.
3. Besides authentication and authorization, the other three mechanisms, i.e., auditing, encryption, and data
masking, are adopted by some industrial (only) systems to enhance security by auditing the actions of users
and encoding and hiding sensitive information. Take again Denodo as an example. The Denodo Platform pro-
vides an audit trail of all the information about the queries and other actions executed on the system. It also
supports the application of strategies on a per-view basis to guarantee secure access to sensitive data through
20https://community.denodo.com/kb/view/document/Denodo%20Security%20Overview
CORRECTED PROOF
22 Z. Gu et al. / A systematic overview of data federation systems
Tab le 4
Evaluation of the data security dimension. Academic systems in italics. “–” denotes feature/information not found in the systems’ official
documentation, websites, or academic publications, to the best of our efforts. Subscript ng denotes the use of named graph-based solutions to
hide (mask) sensitive information in selected graphs to certain users, and possibly (for AnzoGraph DB) expose sanitized named graph views
System Data security
Authentication Authorization Auditing Encryption Data masking
AllegroGraph – – –
Amazon Athena –
Amazon Neptune –
AnzoGraph DB – –
ng
Apache Drill ––
Apache Jena – – – –
Apache Spark ––
BigDAWG – – – – –
Blazegraph – – – – –
CloudMdsQL – – – – –
Comunica – – – – –
CostFed – – – – –
DARQ – – – – –
Data Virtuality – – –
Denodo
Dremio –
FEDRA – – – – –
FedX (RDF4J) – – – –
GraphDB –
HiBISCuS – – – – –
IBM Cloud Pak for Data
IBM Db2 Big SQL –
IBM InfoSphere Federation Server ––
JBoss Data Virtualization –
Metaphactory – – –
Myria – – – – –
Neo4j (Fabric) – – –
Obi-Wan – – – – –
Odyssey – – – – –
Ontario – – – – –
Onto-KIT – – – – –
Oracle Big Data SQL – – –
Oracle DB (Spatial & Graph) –
PolyWeb – – – – –
Presto – –
Querona Data Virtualization –
RDFLib – – – – –
SAFE – – –
SAGE – – – – –
SAP HANA – – – –
SAS Federation Server –
SemaGrow – – – – –
SPLENDID – – – – –
SQL Server (PolyBase) –
Squerall – – – – –
Starburst –
Stardog – –
ng
Teiid ––
TIBCO Data Virtualization ––
Trino ––
Virtuoso ––
Number 32 29 10 20 8
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 23
encryption/decryption at different levels, and it masks (hides) sensitive data to ensure they are not accessed
by unauthorized users. In SPARQL federation engines, data masking is provided by allowing hiding named
graphs with sensitive information to certain users in AnzoGraph DB21 and Stardog;22 in AnzoGraphDB, this
mechanism is complemented by “named views”23 as a way to define (via SPARQL CONSTRUCT queries)
sanitized/masked named graphs to be exposed in place of sensitive ones.
4. Data security has rarely been mentioned in the systems developed by academic and research institutions.
Among the 18 systems we have evaluated in this category, just one system, i.e., SAFE, takes data security
into consideration. SAFE is a SPARQL query federation engine that enables policy-aware access to sensitive,
distributed statistical data sources represented as RDF data cubes.
6.3. Interface dimension
Table 5reports on the evaluation of the interface dimension, which is used to qualitatively evaluate the usability of
the systems from both the end-user and the developer perspectives.As mentioned in Section 4and reflected in the ta-
ble, this dimension comprises the graphical, command line, and application programming interface sub-dimensions.
Here, we analyze which of these interfaces are made available to the users, further identifying the different types of
exposed application programming interfaces (e.g., JDBC drivers, web APIs). We cover only documented (vs. hidden
in the code) interfaces and we do not consider effectiveness and ease of use, whose evaluation is largely subjective
as, for any given interface, user experience is affected by individual user’s preferences and habits. In summary, from
Table 5we can derive the following observations:
1. Nearly all of the industrial systems (31 out of 33, i.e., 94%) provide graphical interfaces, which consist mainly
in web consoles or web interfaces, and command line interfaces (all 33 industrial systems), which help users
to deploy and manage data federation instances. For example, AllegroGraph provides the AllegroGraph Web
View,24 which is a browser-based graphical interface for exploring, querying, and managing AllegroGraph
databases, and Teiid provides users with Teiid Console,25 a web-based administration and monitoring tool.
2. Besides graphical and command line interfaces, most industrial systems like Denodo and Teiid also provide
JDBC and ODBC drivers (respectively, 23 and 18 systems out of 33, i.e., 70% and 55%) to enable users
to access and interact with them as standard relational sources. Web APIs (mainly RESTful) are also very
frequent among industrial systems (25 out of 33, i.e., 76%), while there is less support for ADO.NET and the
SPARQL HTTP API. The latter is exclusively provided by systems supporting the SPARQL query language
(see Table 2) that also directly implement the associated SPARQL HTTP query protocol (instead of relying on
other non-standard means for receiving a SPARQL query and returning its results). Furthermore, few systems,
such as AllegroGraph, Presto and Stardog, provide also multiple client libraries to help users in interfacing
with these systems using the most popular programming languages, like C, Go, Java, Python, R, and Ruby.
3. The three systems not associated to any interface in the table are all academic (Fedra,HiBISCuS,SAFE). For
these systems, the documentation only covers the experiments conducted and indicates, at most, the script
(Fedra) or the code entry points (HiBISCuS) for reproducing the specific experiments.
6.4. Development dimension
Table 6reports on the evaluation of the development dimension and its sub-dimensions, which all together deliver
information relevant to developers for integrating the system with other applications or for patching, extending, or
otherwise modifying the system itself, if possible. Note that for the industrial systems, the information of the first
release, i.e., the year and version number of the first version made available, is actually the information of the oldest
21https://docs.cambridgesemantics.com/anzograph/v2.3/userdoc/acl.htm#Database
22https://docs.stardog.com/operating-stardog/security/named-graph-security
23https://docs.cambridgesemantics.com/anzograph/v2.2/userdoc/named-views.htm
24https://allegrograph.com/products/agwebview/
25https://teiid.github.io/teiid-documents/master/content/admin/Teiid_Console.html
CORRECTED PROOF
24 Z. Gu et al. / A systematic overview of data federation systems
Tab le 5
Evaluation of the interface dimension. Academic systems in italics. “–” denotes feature/information not found in the systems’ official documen-
tation, websites, or academic publications, to the best of our efforts
System Graphical
interface
Command
line interface
Application programming interface
JDBC Driver ODBC Driver Web API ADO.NET SPARQL
HTTP API
AllegroGraph – – –
Amazon Athena – – –
Amazon Neptune –– –
AnzoGraph DB – – –
Apache Drill – –
Apache Jena – – – –
Apache Spark – – –
BigDAWG –– – – –
Blazegraph – – – –
CloudMdsQL – – – – –
Comunica – – – –
CostFed – – – – – –
DARQ –– – – – –
Data Virtuality – –
Denodo –
Dremio – –
FEDRA –– –––––
FedX (RDF4J) – – –
GraphDB ––
HiBISCuS – – – – – – –
IBM Cloud Pak for Data – – – –
IBM Db2 Big SQL – – –
IBM InfoSphere Federation Server –– –
JBoss Data Virtualization – –
Metaphactory – – –
Myria – – – –
Neo4j (Fabric) –– –
Obi-Wan –– – – –
Odyssey –– – – – –
Ontario –– – – – –
Onto-KIT – – – – – –
Oracle Big Data SQL – – – – –
Oracle DB (Spatial & Graph) – – –
PolyWeb – – – – – – –
Presto – –
Querona Data Virtualization ––
RDFLib –– – – – –
SAFE – – – – – – –
SAGE – – – – –
SAP HANA –
SAS Federation Server – –
SemaGrow – – – – –
SPLENDID –– – – – –
SQL Server (PolyBase) ––
Squerall – – – – –
Starburst – –
Stardog – – –
Teiid –
TIBCO Data Virtualization –
Trino – –
Virtuoso
Number 38 45 24 18 27 711
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 25
versions we have been able to gather from their official websites. Note also that the academic systems often do not
follow well-defined release cycles with proper versioning, e.g., CostFed.26 In such situations, we leave their versions
as blank, and fill the years from their commit histories on their GitHub projects. The following are the main insights
we can get from Table 6:
1. Java is the most used programming language for both industrial and academic systems, even when accounting
for the incomplete information of this sub-dimension (see counts in Table 6). Comparatively less used lan-
guages include C/C++ (AnzoGraph DB, SAP HANA and other systems), Python (RDFLib, Squerall, SAGE),
Scala (Apache Spark, Ontario), JavaScript (Comunica) and Lisp (AllegroGraph, in combination with Java).
2. Excluding two SaaS industrial systems from Amazon (Athena, Neptune), on-premises deployment is always
offered, represents the only available option for academic systems, and concerns software both in native form
(nsubscript, almost always possible) and containerized form (csubscript, e.g., via Docker images), the latter
supported more in industrial systems (21 out of 33, i.e., 64%) than academic systems (4 out of 18, i.e., 22%).
SaaS (6 industrial systems out of 33, i.e., 18%) is less frequent than IaaS/PaaS (12 out of 33, i.e., 36%), the
latter always supporting Amazon AWS (asubscript), followed by Microsoft Azure (msubscript, 8 IaaS/PaaS
cases out of 12, i.e., 67%) and Google Cloud Platform (gsubscript, 5 IaaS/PaaS cases out of 12, i.e., 42%).
3. Among the industrial systems, the majority are closed source (21 out of 33, i.e., 64%), and most of these
come with commercial support services (19 systems out of 21, i.e., 90%). Similarly, most of the open source
industrial systems offer the option of commercial support (7 systems out of 12, i.e., 58%). Academic systems
are all open source without commercial support.
4. In comparison with academic systems, it is easy to see that industrial ones typically feature a much more
active development. Some of these industrial systems have been developed, maintained, and improved for
many years, such as Denodo and Teiid. Unfortunately, for the academic systems, despite the fact that all of
them are open source initiatives, it is common that they are not enhanced or maintained after the publication
of the respective academic papers.
6.5. Overall discussion and analysis
Based on the above reported evaluation and analysis, and after having reviewed the official documentation and
academic publications of each of the systems considered in this survey, in the following we summarize the most
crucial and interesting lessons we learned.
Background theory and standards Data federation, especially over heterogeneous data sources, is currently a very
active field in both industry and academia. However, the overall development of data federation systems still seems
to lack background theory and standards. Let us note, for instance, that different systems force users to adopt their
own dialects to develop and model the logical or meta-data layer of the target data sources. This strategy drastically
hinders information reuse, as information produced for one system cannot be directly used in other systems.
Other capabilities Among the other capabilities beyond the data federation task itself (cf. Section 2.2), only data
security was captured by our evaluation framework, which is based solely on the aspects of interest arising from
applying the methodology of Section 3.2. This fact further remarks the importance of data security, especially
among industrial systems, whereas data update and data quality have been less investigated in combination with
the data federation. Nevertheless, some of the considered systems provide capabilities related to data update and
data quality. Concerning data update over the federated data sources, Teiid27 and Denodo28 support INSERT and
DELETE operators, while RDF4J (FedX)29 supports SPARQL UPDATE over the federated SPARQL endpoints.
Other systems mention data update, however it is unclear from the systems’ documentation whether these updates
26https://github.com/dice-group/CostFed
27https://teiid.github.io/teiid-documents/master/content/reference/as_updatable-views.html
28https://community.denodo.com/docs/html/browse/8.0/en/vdp/vql/inserts_updates_and_deletes_over_views/inserts_updates_and_deletes_
over_views
29https://rdf4j.org/documentation/programming/federation/
CORRECTED PROOF
26 Z. Gu et al. / A systematic overview of data federation systems
Tab le 6
Evaluation of development dimension. Academic systems in italics. “F.” and “L.” denote “First” and “Latest” respectively. Subscript letters
further qualify available deployment options: n=native; c=containerized; a=Amazon AWS; m=Microsoft Azure; g=Google Cloud
Platform. “–” denotes feature/information not found in the systems’ official documentation, websites, or academic publications, to the best of
our efforts
System Main development language Deployment Comm.
support
Open
source
Release
C/C++ Java Others On-prem IaaS/PaaS SaaS F. Year F.Version L. Year L. Version
AllegroGraph –Lisp
nc
am ––2004 6.4.0 2021 7.2.0
Amazon Athena –– – –
a–2017 –2021 –
Amazon Neptune –– – –
a–2018 1.0.1.0 2021 1.0.5.1
AnzoGraph DB – –
nc
a–– - 2.0 2021 2.3
Apache Drill ––
nc – – – 2012 M1 2021 1.19
Apache Jena ––
n– – – 2012 2.7.0 2021 4.2.0
Apache Spark – – Scala
nc – – – 2014 1.0 2021 3.2.1
BigDAWG ––
n– – – 2015 –2017 0.0.5
Blazegraph ––
n– – – 2019 2.1.5 2020 2.1.6rc
CloudMdsQL ––
n– – – 2017 –2017 –
Comunica – – JavaScript
nc – – – 2018 1.0.0 2021 1.22.3
CostFed ––
nc – – – 2016 –2018 –
DARQ ––
n– – – 2006 –2008 –
Data Virtuality – – –
nc – – – – – 2021 2.4
Denodo – – –
nc
amg
––2002 1.0 2020 8.0
Dremio ––
nc
am – 2017 1.1 2021 19.0
FEDRA ––
n– – – 2015 –2015 –
FedX (RDF4J) ––
n– – 2011 –2021 3.7.4
GraphDB ––
nc – – –2015 6.2 2021 9.10
HiBISCuS ––
n– – – 2014 12014 1
IBM Cloud Pak for Data – – –
c
amg
–2018 2.1.0 2021 4.0
IBM Db2 Big SQL ––
n––2017 –2020 7.1.0
IBM InfoSphere Federation
Server – – –
n– – – – – 2019 10.5.0
JBoss Data Virtualization ––
nc – – 2014 6.0.0 2018 6.4.0
Metaphactory – – –
a– – – 2015 –2021 4.3.0
Myria ––
n– – – 2014 12017 1
Neo4j (Fabric) ––
nc
amg
2020 4.0.11 2021 4.3.7
Obi-Wan ––
n– – – 2020 –2020 –
Odyssey ––
n– – – 2016 –2019 –
Ontario – – Python
n– – – 2018 –2021 –
Onto-KIT ––
n– – – 2020 –2020 –
Oracle Big Data SQL – – –
n– – – – – 3.0.1 2021 4.1.1
Oracle DB (Spatial &
Graph) – – –
nc – – –2016 –2021 21c
PolyWeb ––
n– – – 2017 –2017 –
Presto ––
nc – – 2013 0.54 2021 0.265.1
Querona Data Virtualization – – –
n– – –2015 –2020 –
RDFLib – – Python
n– – – 2002 1.1.1 2021 6.1.1
SAFE ––
n– – – 2017 –2017 –
SAGE – – Python
nc – – – 2019 1.1 2021 2.3
SAP HANA – –
nc
ag ––2018 1.0.SPS12 2020 2.0.SPS05
SAS Federation Server – –
n– – –2013 3.2 2021 4.4
SemaGrow ––
nc – – – 2014 1.0 2021 2.2.1
SPLENDID ––
n– – – 2011 –2011 –
SQL Server (PolyBase) – –
nc – – –2016 2016 2019 2019
Squerall – – Python
n– – – 2018 0.1 2019 0.2
Starburst ––
nc
amg
––2019 0.188-e 2021 364-e LTS
Stardog ––
nc
a–2011 0.7.3 2021 7.7.3
Teiid ––
nc – – 2009 6.0.0 2020 16.0.0
TIBCO Data Virtualization – – –
nc
am ––2007 7.0.5 2021 8.4.0
Trino ––
nc – – –0.54 2021 364
Virtuoso – –
nc
am –– – – 2020 8.3
Number 531 749 12 626 30 – – – –
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 27
can be performed on the data sources in the federation, or on the data stored locally by the system itself (e.g.,for
database systems extended with federation facilities). Concerning data quality, SAS Federation Server30 supports
methods and rules specified in a “SAS Quality Knowledge Base” (QKB), while Stardog31 supports data quality
constraints expressed in SHACL [128]. Given the current steady growth of data scale and variety, we expect these
aspects to become increasingly important in the context of data federation.
Ontology-based data access Ontologies, providing a shared abstraction of a domain of interest, can play a key role
in handling the heterogeneity of concepts in data integration. The so-called Ontology-Based Data Access (OBDA)
approach has been studied intensively [37,77,129–134] in the last two decades. In OBDA, a mediating ontology
provides a high-level representation of the data contained in a relational source, as well as an encoding of do-
main knowledge. The link between the ontology and the source is realized through mappings, e.g., expressed using
R2RML [135]. The distinctive characteristics of OBDA are that query answers are enriched through automated
reasoning over the ontology, and that such process is carried out in a virtual mode: the data in the database is not
materialized as a graph, but rather queries are rewritten on-the-fly and executed against the original source.
The virtual characteristic of OBDA makes it a potential candidate for incorporating mediating ontologies in the
data federation framework. Still, this marriage has rarely been discussed or considered to its fullest extent, and
it represents an open research line. For instance, Squerall [96] and Pol y We b [32,85] are virtual systems based on
RML/R2RML mappings but both lack reasoning support, hence they do not qualify as fully-fledged OBDA systems
as per their definition in the literature [129]. An exception is Obi-Wan [75,76], an OBDA system32 able to integrate
heterogeneous data sources, including relational, graph-based, and NoSQL ones. Its main idea follows the classical
OBDA framework by first rewriting the original queries based on the ontology and the mappings, and then using
the mediator system Tatooine [78] to evaluate the rewritten queries over multiple and heterogeneous data sources.
Obi-Wan is for the most part a proof-of-concept of a more general and insightful theoretical exercise. Hence, it
does not present any optimization technique specific to the federated setting and is not tailored towards handling
real-world, complex scenarios. Using domain ontologies to virtually integrate heterogeneous data sources combines
the difficulties of ontology reasoning with the ones of integrating heterogeneous data, and this negatively affects
performance. Further investigations and, possibly, innovative approaches are required to obtain systems that would
exhibit a performance that is adequate to real-world application needs. A preliminary investigation towards this
direction has been conducted by Gu et al. [136,137]. The use of ontology-based techniques – and, more generally,
of Semantic Web methods and standards – to address data quality, update, and security aspects of data federation
systems also appears promising and deserves further research.
Interrelationships between data sources Most of the time, the data sources that are subject to a data integration
initiative are not fully independent from each other. Indeed, there may exist interrelationships among the integrated
data sources, such as information overlapping, complementarity, and conflicts. Automatically discovering such in-
terrelationships may help developing data federation systems of higher efficiency. As a simple example, if a data
source S1is part of a data source S2with respect to the metadata layer (both schema and content), then in the query
evaluation procedure S1may be sometimes ignored (e.g., when querying for the union of the content of S1and S2)
and the overall performance improved.
Most advanced methods and systems handle overlapping to some extent. BigDAWG exploits equivalence and
containment information provided by data curators [138] to identify equivalent operations across different data
models, so as to optimize its source-selection strategy. DAW [139], not considered in this survey, exploits a compact
representation of data as vectors for which estimates on overlapping can be automatically found. This information
is then used to prune, with high recall, redundant sources during source selection. FEDRA [64] does not require to
encode data, but relies on fragment descriptions for its source selection, where each fragment essentially describe
the triples that can be extracted out of a set of data sources.
Note that all approaches require a substantial amount of meta-information which might be hard or even impossible
to produce automatically. It has been recently observed by Gu et al. [136,137] that this limitation is greatly reduced
30https://documentation.sas.com/api/docsets/fedsrvag/4.2/content/fedsrvag.pdf
31https://docs.stardog.com/data-quality-constraints
32Although, based on GLAV mappings as opposed to GAV mappings usually applied in OBDA contexts.
CORRECTED PROOF
28 Z. Gu et al. / A systematic overview of data federation systems
in OBDA settings, where one can exploit both the semantic information provided by the ontology and the URI
construction rules encoded in the mappings. This fact allows for optimizations that are not specific to the source
selection phase, such as the removal of redundant or empty operators, or the automatic leveraging of materialization
of pre-computed results and on-the-fly access to the sources.
Combining systems The capabilities of a system can be extended through combination with other tools. We iden-
tify two mechanisms for combining a data federation system with a tool, the latter operating as adapter and possibly
being a data federation system itself; these mechanisms can be iteratively applied to combine multiple components.
In the first mechanism, the tool acts as a source of the system and is used to add indirect support for some ad-
ditional sources that cannot be natively connected to the system, by adapting them to one of the supported source
types (e.g., JDBC or ODBC). For instance, the data sources directly supported by Querona Data Virtualization ex-
clude MongoDB but include Denodo and Apache Drill, which instead support MongoDB and can be thus combined
to add indirect support for MongoDB. As another example, one may extend SPARQL-based federation to rela-
tional sources through the combination with an OBDA engine, as successfully applied by Sima et al. [140] who use
the OBDA system Ontop to expose biomedical data as RDF graphs, then federated through a SPARQL federation
engine.
In the second mechanism, the tool acts as client of the system and is used to adapt or extend the unified schema,
query language(s) or capabilities offered by the system. For example, one may deploy33 an OBDA engine like
Ontop over a SQL-based data federation system such as Dremio or Denodo, so to provide indirect support for
an RDF/OWL unified schema and SPARQL as unified query language. From a complementary perspective, this
combination mechanism can be also seen as adding federation capabilities to the employed tool (the OBDA engine
in the example), effectively giving birth to a new data federation system.
As remarked in the text (Section 6.1), the “Data source” dimension of Table 2and in general all the dimensions
and tables of this survey do not account for the combination of systems, but rather focus solely on sources and
capabilities that are directly supported by the data federation system. The reason is that it is very difficult to com-
prehensively assess which sources or capabilities a data federation system may acquire by carefully combining it
with other tools, as combinations are possibly limitless and the assessment of the practical feasibility of each is
non-trivial and not clearly defined, as there might be hard-to-quantify integration costs involved (e.g., to remove
minor incompatibilities at the interface between combined tools).
7. Related work
In this survey, we have investigated and analyzed a total of 51 data federation systems. Considering data federation
in the broader context of data integration, in the following we situate this survey among other works in the Database
and the Semantic Web literature that review existing approaches, techniques, and systems for both virtual and
materialized data integration.
Database community Halevy et al. [6] discuss some of the most important results in the data integration field before
2006, and outline some challenges for data integration research. The survey by Magnani and Montesi [141] reports
on the techniques for managing uncertainty in data integration, and the survey by Bikakis et al. [142] investigates the
approaches focusing on semi-structured data. Finally, the works by Arputhamary et al. [143–145] mostly address
the issues emerging when techniques and systems are meant to be applied to integrate big data.
Readers that are interested in knowing more about existing approaches and implemented systems for integrating
data virtually can refer to several related surveys [9,42,43,146]. In particular, the survey by Sheth and Larson [9]
discusses data federation systems. The authors define terminology and a “reference architecture” for distributed
database management systems with the main aim of providing a framework in which to understand, categorize,
and compare different architectural options for developing federated database systems. Additionally, they introduce
33https://ontop-vkg.org/tutorial/federation/
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 29
a methodology for developing tightly coupled federated database systems with multiple federations and proces-
sors (that is, software modules that manipulate commands and data). In a different survey, Bondiombouy and Val-
duriez [146] investigate multistore systems by first introducing the currently available cloud data management and
query processing solutions, then describing and analyzing some representative multistore systems according to their
architecture, data model, query languages, and query processing techniques. They finally classify these systems into
three categories, i.e., loosely-coupled, tightly-coupled, and hybrid. The survey by Tan et al. [43] focuses on query
processing over heterogeneous data sources by first introducing a taxonomy that categorizes the solutions into data
federation systems, polyglot systems, multistore systems, and polystore systems. On top of this categorization, the
authors propose an evaluation framework, largely inspired by the work by Sheth and Larson [9], incorporating the
axes of “Heterogeneity”, “Autonomy”, “Transparency”, “Flexibility” and “Optimality”. The survey finally com-
pares and analyzes four specific systems – BigDAWG, CloudMdsQL, Myria, and Apache Drill – according to the
introduced evaluation framework. Azevedo et al. [42] focus on new generation data federation systems addressing
the manipulation of structured and unstructured data, usually in high volume, over distributed and heterogeneous
data sources. The authors first review the literature aiming at giving an overview of state-of-the-art modern data
federation systems and then analyze the four aforementioned systems – BigDAWG, CloudMdsQL, Myria, and
Apache Drill – by reporting on their “Definition”, “Owners”, “Goals”, “Query Specification and Execution”, “Main
Components”, and other significant dimensions.
Semantic web community Wache, Noy, Ekaputra et al. [147–149] provide general surveys of those solutions for
integrating data that are based on Semantic Web technologies and that follow the so-called Ontology-Based Data
Integration (OBDI) approach. OBDI is a broader approach than OBDA, and differs from the latter for the fact of
allowing for very expressive ontology languages while dropping the requirement of virtual access to data. Hence,
OBDI approaches are not really suited to the federation setting considered in this survey. Other works focus instead
on specific subdomains in which semantic technologies have been applied to integrate data. In particular, Buccella
et al. [150] analyze and compare existing approaches for ontology-driven geographic information integration. An
investigation of the approaches and techniques developed in the ontology community for integrating biological data
isgivenbyHassanetal.[151]. The survey by Mountantonakis and Tzitzikas [152] investigates the works that have
been done in the area of Linked Data integration, covering both materialized and virtual integration approaches.
This work provides a concise overview of the issues, methods, tools, and systems for semantic integration of data,
and gives emphasis on the methods that provide support for the integration of large numbers of datasets.
As for the virtual approach to data integration, some literature can be found [20,38–41] surveying, in particu-
lar, approaches and systems for federated SPARQL query answering. To summarize, the survey by Rakhmawati et
al. [38] gives an overview of SPARQL federation frameworks – i.e., frameworks supporting (i) SPARQL 1.1 feder-
ation extension, (ii) federation over SPARQL 1.0 endpoints, and (iii) federation over SPARQL 1.1 endpoints – and
classifies and analyzes 14 existing SPARQL federation approaches. Oguz et al. [20] evaluate 7 federation engines
by first providing a detailed and clear insight on data source selection, join, and query optimization methods. They
also introduce a qualitative comparison of these engines according to the following criteria: “No Preprocessing per
Query”, “Unbound Predicate Queries”, “Parallelization”, and “Adaptive Query Processing”. Ngonga Ngomo and
Saleem [39] provide an overview of current challenges and opportunities of federated query processing as well
as summarize the results of recent state-of-the-art studies. Saleem et al. [40] first provide a survey of 14 feder-
ated SPARQL query engines according to: “Code Availability”, “Implementation Language”, “Licensing”, “Source
Selection Type”, “Join Type”, “Cache”, and “Index/Catalog Update”. They then compare 5 SPARQL endpoint fed-
eration systems by using the performance evaluation framework FedBench [153] and by considering the dimensions
of query runtime, number of sources selected, total number of SPARQL ASK requests used, completeness of an-
swers, and source selection time. Finally, Qudus et al. [41] first propose some metrics to measure the errors in
cardinality estimations of cost-based federation engines and the correlation of the values of these metrics with the
overall query runtimes. Then, they present an empirical evaluation of 5 cost-based SPARQL federation engines on
LargeRDFBench [154] according to the proposed metrics.
Comparison This survey builds on the aforementioned literature and is consistent with the terminology, concepts
and key distinctions adopted therein. For instance, considering the foundational work by Sheth and Larson [9],
CORRECTED PROOF
30 Z. Gu et al. / A systematic overview of data federation systems
their terminology can be related to several (sub-)dimensions of our evaluation framework as follows: (i) “Hetero-
geneity” is captured at different levels by our Data source,Query language,Federation technique and various
Development sub-dimensions; (ii) “Query processing and optimization” is also captured by our Federation tech-
nique sub-dimension; (iii) “Access Control” is related to our Data security dimension (especially, its Authorization
sub-dimension). (iv) “Transparency” is captured by the distinction between Transparent vs. Explicit federation.
The key difference between our work and the aforementioned surveys is mainly reflected in the following two
aspects. First, we have analyzed and investigated a larger number of systems, including among them both industrial
and academic initiatives and systems adopting different data models, i.e., SQL-based and SPARQL-based. Second,
we have introduced here as a novel contribution a framework to inspect, analyze, and then classify the main char-
acteristics of each system. The framework has been developed by taking into consideration the requirements of the
end-users, as well as those of the developers and of the scholars, this way trying to deliver the information that
they need when making choices for their respective data federation activities and projects. Our main motivation is
to assess the techniques and capabilities of the existing systems for data federation, so as to reveal their strengths
and weaknesses in relation to the plurality of evaluation dimensions we consider, rather than classifying the systems
along one single dimension or according to the requirements of one single category of prototypical users.
8. Concluding remarks and future work
In this paper, we provided a systematic overview of 51 data federation systems, with the motivation of evaluating
their capabilities as well as the strengths and weaknesses of the employed techniques for integrating heterogeneous
data sources uniformly and virtually. To do so, we have proposed a framework with four major dimensions and
additional sub-dimensions to classify systems from the end-user, the developer, and the scholar perspectives, in a
uniform and qualitative way. We think that the evaluation framework we have proposed can be valuable for all these
target personas: it helps end-users in finding the system that most suits their application requirements and, at the
same time, it drives decision making by developers and researchers in further improving the currently available
solutions and in designing more powerful federation systems. Besides that, our work also aims at providing up-to-
date reference information for all those interested in dipping their toes in the data federation water.
Integrating and managing heterogeneous data “uniformly and virtually” still have a long way to go both at the the-
oretical and at the practical application levels. Our future work will mainly focus on the following two aspects. In our
current evaluation, efficiency of the investigated systems remains an ignored dimension. Therefore, one direction for
future work is to design extensive experiments to evaluate the performance and assess the restrictions of each sys-
tem in integrating and managing heterogeneous data virtually. On the other hand, it is well known that the Semantic
Web provides standards for both knowledge and data representation and management. However, integrating hetero-
geneous data virtually by relying on semantic technologies and Semantic Web standards still represents an open and
promising research field. The second main direction we want to take is indeed to develop innovative approaches for
ontology-based heterogeneous data integration and management, covering federated query answering, data updates,
security, and data quality assurance, where automated logic-based reasoning techniques play a central role.
Acknowledgements
This research has been partially supported by the EU H2020 project INODE (grant agreement No. 863410), by
the Italian PRIN project HOPE (2019-2022), by the European Regional Development Fund (ERDF) Investment for
Growth and Jobs Programme 2014-2020 through the project IDEE (FESR1133), by the RTD DM 1062/2021 project
OntoCRM, by the Free University of Bozen-Bolzano through the project MP4OBDA, and by the “Fusion Grant”
project HIVE sponsored by Fondazione Cassa di Risparmio di Bolzano and Ontopic s.r.l. in coordination with NOI
Techpark, Südtiroler Wirtschaftsring and Rete Economia Alto Adige. G. Xiao is supported by the Norwegian Re-
search Council via the SIRIUS Centre for Research Based Innovation (grant No. 237898). D. Calvanese is supported
by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wal-
lenberg Foundation. We thank our colleagues, in particular Julien Corman, for their discussions and feedback. We
would also like to thank the reviewers for their valuable feedback and comments on earlier versions of this article.
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 31
Appendix A. Specific data sources supported by the selected systems
Table 7lists the specific sources supported by each investigated data federation system, obtained from available
systems’ documentation and publications. Sources are classified on a local,per-system basis, along the source types
defined in Section 6.1, with additional source information – such as the specific kind(s) of relational, graph-based
or aggregate-oriented system – reported next to the source name via subscript letters (see table caption for legend).
We remark the following:
–Some sources correspond to data access interfaces that can be configured to connect additional systems beyond
the ones explicitly listed in the table. In particular, companies such as CData34 and Progress35 commercialize
connectors for the relational SQL-based JDBC, ODBC, ADO.NET and OLE DB interfaces that can be used
to access a myriad of heterogeneous data sources, possibly different from the ones listed in Table 7(e.g.,
GraphQL sources via specific connectors36) and possibly using a different data model that is transparently
adapted to the relational one by the connector (e.g., , via flattening of nested data). In Table 7, besides the
supported data access interfaces, we explicitly list only the sources that are directly and natively supported by
a system without relying on such third party connectors / adapters.
–Structured files are distinguished from other source types with the same data model (e.g., relational sources
for CSV files, aggregate-oriented – specifically, document-based – for JSON files) by virtue of direct access to
raw file contents by the data federation system. In some cases, however, access to stored structured files may
require metadata services external to the filesystem (e.g., Hive Metadata Store) for locating and interpreting file
contents, or may leverage processing services (e.g., from Hadoop) co-located with the nodes storing the file in
a distributed filesystem (e.g., HDFS), for instance to push down data access operations and computations (e.g.,
filtering, sorting) close to where raw file data reside, this way reducing communication costs.
–Some of the data federation systems investigated in this survey are also listed as supported sources (marked
with ∗subscript) of other systems in Table 7, reflecting the fact that the virtual data sources obtained through
data federation can be used themselves in downstream federations. As a limit case (e.g., AllegroGraph), a
system may list only itself as a supported data source, which occurs when the system offers both storage and
data federation capabilities, and the latter are restricted to instances of the same system.
–Test sources (e.g., emulating /dev/null) and system-specific connectors used to access configuration, per-
formance or log data of the system itself are omitted in Table 7, for simplicity.
Tab le 7
Supported data sources. Academic systems in italics. Additional source information in subscript position: ∗=investigated system; a=special-
ized web API; r=RDF triple store; g=property graph store; k=key-value store; w=wide-column store; d=document store; s=search
engine; h=hardware +software appliance; m=MDX (MultiDimensional eXpressions) support. SPARQLp denotes the SPARQL protocol
System Relational Graph-
based Aggregate-oriented Structured
Files
Web Service
Paradigms Other
AllegroGraph Allegro-
Graphr∗
Amazon
Athena Amazon Redshift, MySQL, PostgreSQL,
Vertica Amazon
Neptunerg∗
Amazon
DocumentDBd,
Amazon
DynamoDBd,
Amazon
OpenSearchs,
HBasew, Redisk
Common Log
Format, CSV ,
JSON, ORC ,
Parquet
Amazon AWS
System Manager
Inventorya, Amazon
CloudWatcha,
Amazon Timestream
Amazon
Neptune SPARQLp
AnzoGraph
DB
Derby, Google BigQuery , Hive, HSQLDB,
IBM DB2, Impala , JDBC, MariaDB, MS SQL
Server∗, MySQL, PostgreSQL, SAP ASE SPARQLp
CSV, JSON ,
Parquet,
SAS7BDAT,
SAS XPT,
XML
HTTP /
REST
34https://www.cdata.com/drivers/
35https://www.progress.com/connectors
36https://www.cdata.com/drivers/graphql/
CORRECTED PROOF
32 Z. Gu et al. / A systematic overview of data federation systems
Tab le 7
(Continued)
System Relational Graph-
based Aggregate-oriented Structured
Files
Web Service
Paradigms Other
Apache Drill Derby, Druid , Hive, H2, MS SQL Server∗,
MySQL, Oracle DB∗, PostgreSQL
Cassandraw,
Elasticsearchs,
HBasew,MapR-DB
w,
MongoDBd, Splunks
Avro,
Common Log
Format, CSV ,
Excel, JSON ,
Parquet,
SequenceFile,
XML
HTTP /
REST Kafka, OpenTSDB
Apache Jena Jena API,
SPARQLp
Apache Spark Hive, JDBC
any file
(content field
+ metadata),
Avro, CSV,
JSON, ORC ,
Parquet
BigDAWG PostgreSQL AccumulowSciDB
Blazegraph SPARQLp
CloudMdsQL Derby SparkseegMongoDBd
Comunica SPARQLp,
TPF RDF
CostFed SPARQLp
DARQ SPARQLp
Data
Virtuality
Amazon Redshift, ClickHouse , Data
Virtuality∗, Derby, Exasol, Google BigQuery ,
Greenplum, Hive, HSQLDB , H2, IBM DB2,
IBM Informix, IBM Netezzah, Ingres , JDBC,
MDXm, MetaMatrix∗, MS SQL Server∗,
MySQL, Oracle DB∗, PostgreSQL , SAP ASE,
SingleStore, Snowflake, Teradata
Neo4jg∗MongoDBd, RediskCSV, Excel ,
JSON, XML HTTP /
REST
DHL Track & Tracea,
Google Adsa, Google
Analyticsa,
InterSystems Caché,
Kdb+, LDAP,
ModeShape,
Salesforcea
Denodo
Amazon Athena∗, Amazon Redshift,
Databricks, Denodo∗, Derby , Google
BigQuery, Greenplum , Hive, IBM DB2, IBM
Informix, IBM Netezzah, Impala , JDBC, MS
Analysis Servicem, MS Azure SQL Database,
MS SQL Server∗, MS Azure Synapse
Analytics, Mondrianm, MySQL , Oracle DB∗,
Oracle Essbasem, Oracle TimesTen,
PostgreSQL, Presto∗, SAP ASE , SAP Business
Warehousem, SAP HANA∗, Snowflake,
Teradata, Trino∗, Vertica, Yellowbrickh
Amazon
OpenSearchs,
Cassandraw,
Elasticsearchs,
MongoDBd
CSV, Excel ,
JSON, XML SOAP /
WSDL
ITPilot (website
wrapper generator),
LDAP, Salesforcea,
SAP Businessa
Dremio Amazon Redshift, Hive, MS SQL Server∗,
MySQL, Oracle DB∗, PostgreSQL , Teradata
Amazon
OpenSearchs,
Elasticsearchs,
HBasew, MongoDBd
CSV, Excel ,
JSON,
Parquet
FEDRA SPARQLp
FedX (RDF4J) RDF4J
API,
SPARQLp
GraphDB IBM DB2, MS SQL Server∗, MySQL , Oracle
DB∗, PostgreSQL GraphDBr,
SPARQLp
HiBISCuS SPARQLp
IBM Cloud
Pak for Data
Amazon Redshift, Derby , Google BigQuery,
Greenplum, Hive, IBM DB2, IBM Db2 Big
SQL∗, IBM Db2 Warehouse, IBM DVM, IBM
Informix, IBM Netezzah, Impala , MariaDB,
MS SQL Server∗, MySQL, Oracle DB∗,
PostgreSQL, SAP ASE , SAP HANA∗,
Snowflake, Teradata
MongoDBdCSV, Excel OData IBM Db2 Event
Store, Salesforcea,
SAP Gateway ODataa
IBM Db2 Big
SQL
Amazon Athena∗, Amazon Redshift, Derby ,
Google BigQuery, Greenplum , Hive, IBM
DB2, IBM Db2 Big SQL∗, IBM Db2
Warehouse, IBM DVM, IBM Informix, IBM
Integrated Analytics Systemh, IBM Netezzah,
IBM PureDatah, Impala, MariaDB , MS Azure
SQL Database, MS SQL Server∗, MySQL,
Oracle DB∗, PostgreSQL, SAP ASE , SAP
HANA∗, Teradata
Amazon
OpenSearchs,
CouchDBd,
MongoDBd
Parquet IBM MQ,
Salesforcea
IBM
InfoSphere
Federation
Server
IBM DB2, IBM Informix , MS SQL Server∗,
Oracle DB∗, SAP ASE, Datacom/DB ,
Teradata, IBM Netezzah
Excel, XML SOAP /
WSDL BioRS, IBM MQ ,
IDMS, IMS
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 33
Tab le 7
(Continued)
System Relational Graph-
based Aggregate-oriented Structured
Files
Web Service
Paradigms Other
JBoss Data
Virtualization
Actian Vector , Amazon Redshift, Exasol,
Greenplum, Hive, Hive, IBM DB2, IBM
Informix, IBM Netezzah, Impala , Ingres,
JBoss Data Virtualization∗, MariaDB,
MetaMatrix∗, MS Access, MS SQL Server∗,
Mondrianm, MySQL, Oracle DB∗,
PostgreSQL, Presto∗, SAP ASE , SAP
HANA∗, SAP IQ, Teradata, Vertica
Accumulow, Amazon
OpenSearchs,
Cassandraw,
Couchbased,
HBasew, MongoDBd,
Red Hat Data Gridk,
Solrs
CSV, Excel ,
XML
HTTP /
REST,
OData,
SOAP /
WSDL
Google Sheetsa,
LDAP, ModeShape,
OSIsoft PI, Red Hat
Directory Server,
Salesforcea,SAP
Gateway ODataa
Metaphactory JDBC
Amazon
Neptunerg∗,
GraphDBr,
SPARQLp,
Stardogr∗,
Virtuosor∗
ElasticsearchsHTTP /
REST
Myria SPARQLp Amazon OpenSearchsCSV SciDB
Neo4j
(Fabric) Neo4jg∗
Obi-Wan PostgreSQL Jena TDBrMongoDBd, Redisk
Odyssey SPARQLp
Ontario MySQL Neo4jg∗,
SPARQLp MongoDBdCSV, XML
Onto-KIT CSV, ENVI ,
JSON
Oracle Big
Data SQL Hive HBasew, Oracle
NoSQLk
Avro, CSV,
JSON, ORC ,
Parquet,
XML
Kafka
Oracle DB
(Spatial &
Graph) Oracle DB∗SPARQLp
PolyWeb MySQL SPARQLp CSV
Presto Amazon Redshift, Druid , Google BigQuery,
Hive, Iceberg, Kudu , MS SQL Server∗,
MySQL, Oracle DB∗, Pinot , PostgreSQL
Accumulow,
Cassandraw,
Elasticsearchs,
MongoDBd, Redisk
Kafka, Prometheus
Querona Data
Virtualization
Actian Matrix, Actian Vector, ADO.NET,
Alibaba AnalyticDB for MySQL, Alibaba Data
Lake Analytics, Amazon Athena∗, Amazon
Aurora, Amazon Redshift , ClickHouse,
Databricks, dBASE, Denodo∗, Drill∗, Exasol,
Google BigQuery, IBM DB2 , JDBC,
MariaDB, MS Access , MS SQL Server∗,MS
Azure Synapse Analytics, MySQL , ODBC,
OLE DB, Oracle DB∗, PostgreSQL , SAP
HANA∗, SAS Scalable Performance Data
Server, Spark∗, Teradata, Teradata Aster,
Vertica
Amazon
OpenSearchs,
DataStaxw
CSV, Excel ,
MSG/EML
(email),
PDF (metadata)
Kafka
RDFLib SPARQLp
SAFE SPARQLp
SAGE SPARQLp
SAP HANA
Amazon Athena∗, Google BigQuery, IBM
DB2, IBM Netezzah, MS SQL Server∗, Oracle
DB∗, SAP ASE, SAP HANA∗, SAP IQ, SAP
MaxDB, Teradata
SAP HANA
Streaming Analytics
SAS
Federation
Server
dBASE, Greenplum, Hive, IBM DB2, IBM
Informix, IBM Netezzah, Impala , MS Access,
MS SQL Server∗, MySQL, Oracle DB∗,
Paradox, PostgreSQL, Progress OpenEdge
RDBMS, SAP ASE , SAP HANA∗,SAS
Federation Server∗, SAS Scalable Performance
Data Server, Teradata
Btrieve, Salesforcea,
SAP RFCa
SemaGrow SPARQLp
SPLENDID SPARQLp
SQL Server
(PolyBase) MS SQL Server∗, ODBC, Oracle DB∗,
Teradata MongoDBd
CSV, JSON ,
ORC,
Parquet,
RCFile
Squerall MySQL
Cassandraw,
Couchbased,
Elasticsearchs,
MongoDBd
CSV, Parquet
CORRECTED PROOF
34 Z. Gu et al. / A systematic overview of data federation systems
Tab le 7
(Continued)
System Relational Graph-
based Aggregate-oriented Structured
Files
Web Service
Paradigms Other
Starburst
Amazon Redshift, ClickHouse , Druid, Google
BigQuery, Greenplum , Hive, IBM DB2, IBM
Netezzah, Iceberg, JDBC, Kudu, MS SQL
Server∗, MS Azure Synapse Analytics,
MySQL, Oracle DB∗, Pinot , PostgreSQL, SAP
HANA∗, SingleStore, Snowflake, Starburst∗,
Teradata, Vertica
Accumulow, Amazon
DynamoDBd,
Cassandraw,
Elasticsearchs,
HBasew, MongoDBd,
Redisk, Splunks
Avro, CSV,
JSON, ORC ,
Parquet,
RCFile,
SequenceFile
Amazon Kinesis,
Google Sheetsa,
Kafka, Prometheus ,
Salesforcea
Stardog
Amazon Athena∗, Amazon Aurora, Amazon
Redshift, Derby , Exasol, Google BigQuery,
Hive, H2, IBM DB2, Impala, MariaDB, MS
SQL Server∗, MySQL, Oracle DB∗,
PostgreSQL, SAP ASE , SAP HANA∗,
Snowflake, Teradata
SPARQLp,
Stardogr∗
Amazon
OpenSearchs,
Cassandraw,
DataStaxw,
Elasticsearchs,MS
Azure Cosmos DBd,
MongoDBd, Splunks
CSV, JSON Google Sheetsa,
Jiraa,LDAP,
Salesforcea
Teiid
Actian Vector , Amazon Athena∗, Amazon
Redshift, Derby , Exasol, Greenplum, Hive,
HSQLDB, H2 , IBM DB2, IBM Informix , IBM
Netezzah, Impala, Ingres , JDBC, MariaDB ,
MDXm, MetaMatrix∗, MS Access, MS SQL
Server∗, Mondrianm, MySQL, Oracle DB∗,
PostgreSQL, Presto∗, SAP ASE , SAP
HANA∗, SAP IQ, Teiid∗, Teradata, Vertica
Accumulow, Amazon
OpenSearchs,
Amazon
SimpleDBkw,
Cassandraw,
Couchbased,
HBasew, Infinispank,
MongoDBd, Solrs
CSV, Excel ,
JSON, XML
HTTP /
REST,
OData,
OpenAPI,
SOAP /
WSDL
Google Sheetsa,
InterSystems Caché,
JPA/JPQL sources,
LDAP, MS Active
Directory,
ModeShape, OSIsoft
PI, Red Hat Directory
Server, Salesforcea,
SAP Gateway ODataa
TIBCO Data
Virtualization
Amazon Redshift, Drill∗, Google BigQuery ,
Greenplum, Hive, HP Neoviewh, HSQLDB,
IBM DB2, IBM Informix , IBM Netezzah,MS
Access, MS SQL Server∗, MySQL, Oracle
DB∗, PostgreSQL, SAP ASE , SAP Business
Warehousem, SAP HANA∗, Snowflake,
Teradata, Tibco ComputeDB,
TibcoDataVirtualization∗, Vertica
Amazon
DynamoDBd,
Amazon
OpenSearchs,
Cassandraw,
Couchbased,
Elasticsearchs,
HBasew,
MarkLogicd,MS
Azure Cosmos DBd,
MongoDBd, Splunks
CSV, Excel ,
JSON, XML
HTTP /
REST,
OData,
SOAP /
WSDL
Eloquaa, Facebooka,
Google Adsa, Google
Analyticsa, Google
Calendara, Google
Contactsa, Google
Sheetsa, HubSpota,
IMAP, Marketoa,MS
Sharepointa,MS
Sharepoint Excel
Servicesa, NetSuitea,
RSS, Salesforcea,
SAP RFCa, Twittera
Trino
Amazon Redshift, ClickHouse , Druid, Google
BigQuery, Hive, Iceberg , Kudu, MS SQL
Server∗, MySQL, Oracle DB∗, Pinot,
PostgreSQL, SingleStore
Accumulow,
Cassandraw,
Elasticsearchs,
HBasew, MongoDBd,
Redisk
Amazon Kinesis,
Google Sheetsa,
Kafka, Prometheus
Virtuoso
Firebird, IBM DB2 , IBM Informix, Ingres,
MS SQL Server∗, MySQL, Oracle DB∗,
PostgreSQL, Progress OpenEdge RDBMS,
SAP ASE
SPARQLp
Appendix B. Selection of academic systems
We report further details about the academic systems selection process described in Section 3.1, providing: (i) the
statistics of the 295 academic publications found in our literature search (Section B.1); (ii) the metadata and the con-
sidered systems and aspects for the 17 system comparison publications found among them (Section B.2); (iii) the
inclusion criteria satisfied or violated by the 56 academic systems found, which support our selection of 18 aca-
demic systems (Section B.3); and (iv) the full bibliography of all the 295 collected academic publications (Sec-
tion B.4).
B.1. Statistics of collected publications
Table 8reports the breakdown of the 295 academic publications collected in our literature review, grouped by
year and venue. We distinguish between journals,conferences,workshops and others venue categories, the latter
comprising PhD thesis, poster and demo papers, technical reports, book chapters and so on. We also highlight the
more frequent venues for each category, such as ISWC for conferences, to provide some insight about where the
data federation topics of this survey have been mostly discussed. The resulting venues include the main journals and
conferences of the Semantic Web and Database areas, as well as some of their co-located workshops.
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 35
Tab le 8
Statistics of collected publications
Fig. 6. Number of publications by venue type, both overall (left pie chart) and by year (right stacked area chart).
CORRECTED PROOF
36 Z. Gu et al. / A systematic overview of data federation systems
Figure 6provides a graphical depiction of the information in Table 8. The pie chart on the left shows that most
of the publications found are from conference proceedings. Instead, the stacked area chart on the right suggests
an overall increasing trend of yearly publications on data federation in the representative period 2011–2019. Note
that before 2011 we do not have enough data so we grouped all years together, while after 2019 counts are not
informative as the bulk of our literature review was conducted between the end of 2020 and the beginning of 2021,
so we missed some late 2020 publications and we collected only a very small sample of 2021 publications (e.g.,
articles from “online first” venues that were already online when we conducted our literature search).
B.2. System comparison publications
Table 9lists the 17 system comparison publications contained in the retrieved 295 academic publications. They
include surveys, benchmarks, evaluation papers and PhD theses focusing on data federation topics and involving
extensive qualitative and/or quantitative comparison of multiple data federation systems. For each system compari-
son publication, we report in Table 9: (i) its title, venue and year metadata; (ii) the number of citations from Google
Scholar (as of 2022/06/07); (iii) the investigated systems, this information being used in our relevance criterion for
system selection (Section 3.1); and (iv) the aspects of interest analyzed in the publication, this information being
exploited in our methodology for the design of the system evaluation framework (Section 3.2). Note that for certain
publications, some aspects (marked with ∗) are investigated only for a subset of the considered systems (also marked
with ∗). Additionally, two surveys [38,40] can be also found in revised form but with the same systems and aspects
considered, in the PhD thesis of the respective authors (this is indicated in the Title column of Table 9).
B.3. Academic systems
Table 10 lists all the 56 academic systems identifed starting from the 295 academic publications, with the names
of selected systems highlighted in bold. Systems are sorted by year, which we conventionally set as the publication
year of the most recent conference or journal paper about the system. Besides the system name, Table 10 contains
all the information considered for selecting or discarding a system based on the inclusion criteria of Section 3.1:
–column C.A. denotes code availability, which is a mandatory requirement for a system being selected;
–columns Sur,Het,Sec denote the system respectively being mentioned by a survey of Table 9(in subscript the
number of mentioning surveys), supporting heterogeneous data sources, or considering data security; at least
one of these features must be present for a system being selected;
–column # Cit. denotes the number of citations from Google Scholar (as of 2022/06/07), summed over all
the publications about the system; this number must be the largest one for periods 2008, 2009–2011, and
2012–2014, or above the threshold of 10 for the period 2015–2019 (no constraint set for period 2020);
–column Publication, finally, lists the academic publications about the system, for which we require formal (i.e.,
peer-reviewed) publications for a system being selected.
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 37
Tab le 9
Collected system comparison publications. Cit. = citations (2022/06/07); ∗= restriction to selected aspects/systems; bold = systems in our survey
Title Venues Ye a r Cit. Considered systems Assessed dimensions
FedBench: A benchmark suite for
federated semantic data query
processing [153]ISWC 2011 194 SPLENDID, Sesame AliBaba evaluation time, # endpoint requests
A comparison of federation over SPARQL
endpoints frameworks [38]
also available as PhD thesis
chapter [155]
KESW 2013 21
ADERIS, ANAPSID, Avalanche,
DARQ, Distributed SPARQL,
FedX, GDS, Jena , SemWIQ,
Sesame AliBaba, SPARQL-DQP,
SPLENDID,Virtuoso, WoDQA
catalog, platform, source selection, cache,
query execution (join types), source tracking,
GUI
An holistic evaluation of federated
SPARQL query engine [156]ISICO 2013 2DARQ,Fed X,SPLENDID
response time, amount of data sent/received,
size of intermediate results, # requests,
# ASK requests, # sources selected, avg. size
of intermediate results, max rows retrieved,
requests workload, avg. data received
QFed: Query set for federated SPARQL
query benchmark [157]iiWAS 2014 13 FedX,Jena data transmission, run time
On metrics for measuring fragmentation
of federation over SPARQL
Endpoints [158]WEBIST 2014 7DARQ,SPLENDID data transfer, requests workload
Distributed query processing for federated
RDF data [159]
PhD thesis
Univ.
Koblenz-
Landau
2015 3
ANAPSID, Avalanche, DARQ,
FeDeRate, FedX, Mediator
SAIL, Min-Tree BGP, MisMed,
Networked Graphs, Prasser at al.,
QTree, SemWIQ, SPARQL-DQP,
SPLENDID
catalog, source selection, query optimization
(heuristics or cost-based), query execution
(join types)
Federated query processing on Linked
Data: A qualitative survey and open
challenges [20]
Knowl.
Eng. Rev. 2015 32 ADERIS, ANAPSID, DARQ,
FedX, LHD, SPLENDID,
WoDQA
data source selection, join methods, query
optimization
A fine-grained evaluation of SPARQL
endpoint federation systems [40]
also available as PhD thesis
chapter [160,161]
Semantic
Web 2016 125
ADERIS∗, ANAPSID∗, Atlas,
Avalanche, DARQ∗,DAW,
FedSearch, FedX∗,
GRANATUM, LDQPS, LHD∗,
SIHJoin, SPLENDID∗,
WoDQA,
category, code availability, implementation
language, license, source selection type,
join type, cache, index/catalog update,
system’s features (results completeness and
duplicate detection), supported SPARQL
constructors # sources selected∗,#ASK
requests∗, result completeness∗, source
selection time∗, query execution time∗,
overall performance∗, effect of data
partitioning∗
LargeRDFBench: A billion triples
benchmark for SPARQL endpoint
federation [154]
J. Web
Semant. 2018 57 ANAPSID, FedX,HiBISCuS,
SPLENDID
source selection, result set completeness and
correctness, query execution time
Extending LargeRDFBench for
multi-source data at scale for SPARQL
endpoint federation [162]
SSWS @
ISWC 2018 4ANAPSID, CostFed,FedX,
HiBISCuS,SemaGrow,
SPLENDID
# source selection (TPWSS), # ASK requests,
source selection time, runtime
Enabling query processing across
heterogeneous data models: A survey [43]BigData 2017 101 Apache Drill,BigDAWG,
CloudMdsQL,Myria
heterogeneity, autonomy, transparency,
flexibility, optimality
An empirical evaluation of cost-based
federated SPARQL query processing
Engines [41]
Semantic
Web 2019 1
ANAPSID, BioFed, CostFed∗,
DARQ,FedX, LHD∗, Lusail,
MULDER, Odyssey∗,
SemaGrow∗,SPLENDID∗
index, query processing, network (i.e., #
transferred tuples), result set, resources,
errors/q-error of triple patterns∗,
errors/q-error of joins between triple
patterns∗, errors/q-error of overall query
plans∗, overall query runtime∗, # tuples
transferred∗, source selection metrics∗,
quality of generated plans∗
Large scale semantic integration of Linked
Data: A survey [152]
ACM
Comput.
Surv. 2019 54 ANAPSID, DARQ,DAW,
FedX,HiBISCuS, MULDER,
SPLENDID
data set types, output types, transformations,
schema matching, instance matching,
provenance levels, quality, evolution, tested
data sets and scalability
Federated query processing over
heterogeneous data sources in a semantic
data lake [163]
PhD thesis
Univ.
Bonn 2020 3
ANAPSID, Avalance, DARQ,
DAW, FEDRA,FedX,
HiBISCuS, Lusail, Odyssey,
SAFE,Semagrow,SPLENDID
catalog, ASK-based source selection,
privacy awareness
Modern federated database systems: An
overview [42]ICEIS 2020 3Apache Drill,BigDAWG,
CloudMdsQL,Myria
definition, owner, goal, internal data
representation and platform for data
operations, context segregation, query
specification and execution, heterogeneity,
main components, demonstration
Identifying, relating, consisting and
querying large heterogeneous RDF
sources [164]
PhD thesis
Univ.
Leipzig 2021 –
ADERIS, ANAPSID,
Comunica,CostFed,DARQ,
DAW, FEDRA,FedX, LDQPS,
Lusail, MULDER, Odissey,
SaGe,SemaGrow, SIHJoin,
SOUIN, SPLENDID,TPF
Client, WimuQ, WoDQA
SPARQL query federation, link traversal
based SPARQL federation, dataset
identification, source selection, duplicate
awareness
CORRECTED PROOF
38 Z. Gu et al. / A systematic overview of data federation systems
Tab le 10
Academic systems selection. C.A. =code availability; Sur =mentioned by Table 9surveys; Het =data heterogeneity; Sec =data security;
Cit. =citations (2022/06/07); bold =systems selected for our survey
Relevance
System C.A. Sur Het Sec # Cit. Publications
DARQ 12 – – >500 ESWC 2008 [45]
Mediator SAIL –1– – 71 IJWET 2005 [165]
Distributed SPARQL –1– – 36 ISWC 2008 (poster&demos) [166]
Networked Graphs –1– – 133 WWW 2008 [167]
2008
SemWIQ –2– – 133 ESWC 2008 [168]
SPLENDID 16 – – 307 COLD 2011 [46]
FeDeRate 1– – 74 BMC Bioinf. 2009 [169]
Atlas –1– – 53 J. Web Semant. 2010 [170]
QTree –1– – 259 WWW 2010 [171]
LDQPS –2– – 149 ISWC 2010 [172]
ANAPSID 11 – – 237 ISWC 2011 [173]
ADERIS –1– – 43 OTM 2011 [174]
GDS 1– – 3 tech. rep. U. Southampton 2011 [175]
Min-Tree BGP 1– – 24 JIST 2011 [176]
SIHJoin –2– – 84 ESWC 2011 [177]
2009 -
2011
SPARQL-DQP 2– – 101 ESWC 2011 [178]
HiBISCuS 4– – 150 ESWC 2014 [66]
Prasser et al. –1– – 150 EDBT 2012 [179]
DISMED 1– – 6 IEEE Trans. Inf. Technol. Biomed. 2012 [180]
WoDQA 4– – 6 LDOW 2012 [181]
GRANATUM 4– – 113 ISWC 2014 [182], JIST 2014 [183], OEDW@EKAW 2014 [184]
LHD –5– – 54 LDOW 2013 [185]
FedSearch –1– – 35 ISWC 2013 [186]
DAW –4– – 95 ISWC 2013 [139]
SOUIN 1– – 50 SIGMOD 2013 [187]
2012 -
2014
Avalance –1– – 100 ISWC (posters&demo) 2010 [188], J. Web Semant. 2014 [189]
FEDRA 2– – 40 ISWC 2015 [64]
SemaGrow 5– – 70 SEMANTICS 2015 [93]
oLinDa – – – 1 J. Inf. Data Manag. 2015 [190]
CloudMdsQL 2–159 SIGMOD 2016 [35], Distr. Parall. Datab. 2016 [59], CLOSER 2016 [191]
BigDAWG 2–>500 HPEC 2017 [57], VLDB 2015 [192], SIGMOD Record 2015 [33], etc.
SPARQL-LD –––45 TPDL 2016 [193], ISWC (posters&demo) 2016 [194], QuWeDa@ESWC 2018 [195]
FuhSen ––– 25 WWW 2016 [196]
Odyssey 2– – 50 ISWC 2017 [79]
SAFE 1–96 J. Biom. Sem. 2017 [89], SWAT5LS 2014 [197]
Lusail –3– – 41 VLDB 2017 [198], SIGMOD 2017 [199], ICDE 2017 [200]
Myria 2– – 217 CIDR 2017 [34], SIGMOD 2014 [201]
LILAC ––– 22 J. Web Semant. 2017 [196]
BioFed –1– – 44 J. Biomedical Semant. 2017 [202]
Comunica (TPF-Client) 1– – 393 ISWC 2018 [60], J. Web Semant. 2016 [203]
CostFed 4– – 40 SEMANTiCS 2018 [61], ISWC (posters&demos) 2017 [204]
BOUNCER – – 6DEXA 2018 [205]
SPARQL Micro-Services ––– 18 LDOW@WWW 2018 [206]
PolyWeb ––40 IEEE Access 2019 [32], Big Data 2017 [85]
Ontario ––65 DEXA 2019 [28], DEXA 2017 [80]
SAGE 1– – 39 WWW 2019 [90]
Squerall ––58 ISWC 2019 [96], WWW 2019 [208], iiWAS 2019 [209], ISWC (satellites) 2019 [207]
FMQO – – – 9 APWeb 2019 [210], DASFAA 2018 [211]
WimuQ 1– – 6 K-CAP 2019 [212]
SpecINT – – – 2 Semant. Web 2019 [213]
LDaQ – – – 4 SAC 2019 [214]
Polystore++ –– 2 ICDCS 2019 [215]
2015 -
2019
SemKT – – – 8 IEEE Big Data 2019 [216]
Obi-Wan ––25 EDBT 2020 [75], VLDB 2020 [76]
Onto-Kit –– 6 FGCS 2020 [81]
2020
FLiQue – – – 1 BDA 2020 [217]
CORRECTED PROOF
Z. Gu et al. / A systematic overview of data federation systems 39
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2. I. Abdelaziz, E. Mansour, M. Ouzzani, A. Aboulnaga and P. Kalnis, Query optimizations over decentralized RDF graphs, in: 33rd IEEE
International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, April 19–22, 2017, IEEE Computer Society, 2017,
139–142. doi:10.1109/ICDE.2017.59
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Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference 2017 (Demonstrations),
Chicago, IL, USA, May 14–19, 2017, S. Salihoglu, W. Zhou, R. Chirkova, J. Yang and D. Suciu, eds, ACM, 2017, pp. 1603–1606.
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