Riccardo Tommasini

Riccardo Tommasini
Institut National des Sciences Appliquées de Lyon | INSA Lyon · Department of Computer Sciences

PhD Computer Science

About

41
Publications
6,250
Reads
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211
Citations
Citations since 2016
41 Research Items
211 Citations
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2016201720182019202020212022020406080
2016201720182019202020212022020406080

Publications

Publications (41)
Preprint
Prescriptive Performance Analysis (PPA) has shown to be more useful than traditional descriptive and diagnostic analyses for making sense of Big Data (BD) frameworks' performance. In practice, when processing large (RDF) graphs on top of relational BD systems, several design decisions emerge and cannot be decided automatically, e.g., the choice of...
Conference Paper
Full-text available
Leveraging Big Data (BD) processing frameworks to process large-scale Resource Description Framework (RDF) datasets holds a great interest in optimizing query performance. Modern BD services are complicated data systems, where tuning the configurations notably affects the performance. Benchmarking different frameworks and configurations provides th...
Preprint
Full-text available
The scientific community has been studying graph data models for decades. Their high expressiveness and elasticity led the scientific community to design a variety of graph data models and graph query languages, and the practitioners to use them to model real-world cases and extract useful information. Recently, property graphs and, in particular,...
Article
Full-text available
Ensuring the success of big graph processing for the next decade and beyond.
Book
Full-text available
This book constitutes the proceedings of the satellite events held at the 18th Extended Semantic Web Conference, ESWC 2021, in June 2021. The conference was held online, due to the COVID-19 pandemic. During ESWC 2021, the following six workshops took place: 1) the Second International Workshop on Deep Learning meets Ontologies and Natural Language...
Chapter
Full-text available
The RDF Stream Processing (RSP) community has proposed several models and languages for continuously querying and reasoning over RDF streams over the last decade. They each have their semantics, making them hard to compare. The variety of approaches has fostered both empirical and theoretical research and led to the design of RSPQL, i.e., a unifyin...
Conference Paper
Full-text available
This paper discusses one of the most significant challenges of large-scale RDF data processing over Apache Spark, the relational schema optimization. The choice of RDF partitioning techniques and storage formats using SparkSQL significantly impacts query performance. The impact of the relational schemas and the underlying data storage formats is in...
Chapter
Full-text available
A new generation of Web Applications is pushing the Web infrastructure to process data as soon as they arrive and before they are no longer valuable. However, the Web infrastructure as it is not adequate, and Stream Processing technologies cannot deal with heterogeneous data streams and events. To solve these issues, we need to investigate how to i...
Preprint
Full-text available
Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the...
Article
Full-text available
Nowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards being the ultimate framework for streaming analytics. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP). The former enables stateful computatio...
Poster
Full-text available
Linked Data reveals the need for big semantic data processing. The underlying literature already discusses numerous attempts at leveraging the relational engines of Big Data frameworks like Apache Spark to run SPARQL queries at scale. However, the choice of a relational schema to store RDF data may significantly impact the query performance and hen...
Chapter
Full-text available
Alongside with the ongoing initiative of FAIR data management, the problem of handling Streaming Linked Data (SLD) is relevant as never before. The Web is changing to tame Data Velocity and fulfill the needs of a new generation of Web applications. New protocols (e.g. WebSockets and Server-Sent Events) emerge to grant continuous and reactive data a...
Chapter
Full-text available
Stream Reasoning is set at the confluence of Artificial Intelligence and Stream Processing with the ambitious goal to reason on rapidly changing flows of information. The goals of the lecture are threefold: (1) Introducing students to the state-of-the-art of Stream Reasoning, (2) Deep diving into RDF Stream Processing by outlining how to design, de...
Preprint
Full-text available
Data Velocity reached the Web shore. Indeed, the Web is already adding new protocols and APIs (e.g. WebSockets, and EventSource). The Web of Data is also evolving to tame Velocity without neglecting Variety. The RDF Stream Processing (RSP) community is actively addressing these challenges by proposing continuous query languages and working prototyp...
Conference Paper
Full-text available
Recently, a wide range of Web applications (e.g. DBPedia, Uniprot, and Probase) are built on top of vast RDF knowledge bases and using the SPARQL query language. The continuous growth of these knowledge bases led to the investigation of new paradigms and technologies for storing, accessing, and querying RDF data. In practice, modern big data system...
Chapter
Full-text available
Web Stream Processing (WSP) is a field that studies how to identify, access, represent and process flows of data using Web technologies. One of the barriers that currently limits the adoption of WSP is the paradigm shift from Web data at-rest to Web data in-motion. This barrier is especially high when teaching undergraduate students. To quantify th...
Conference Paper
Full-text available
Data Velocity reached the Web. New protocols and APIs (e.g. WebSockets, and EventSource) are emerging, and the Web of Data is also evolving to tame Velocity without neglecting Variety. The RDF Stream Processing (RSP) community is actively addressing these challenges by proposing continuous query languages and working prototypes. Nevertheless, the p...
Conference Paper
Full-text available
Recently, a wide range of Web applications (e.g. DBPedia, Uniprot, and Probase) are built on top of vast RDF knowledge bases and using the SPARQL query language. The continuous growth of these knowledge bases led to the investigation of new paradigms and technologies for storing, accessing, and querying RDF data. In practice, modern big data system...
Conference Paper
Full-text available
Many domains, such as the Internet of Things and Social Media, demand to combine data streams with background knowledge to enable meaningful analysis in real-time. When background knowledge takes the form of taxonomies and class hierarchies, Semantic Web technologies are valuable tools and their extension to data streams, namely RDF Stream processi...
Conference Paper
Full-text available
In the Big Data context, data streaming systems have been introduced to tame velocity and enable reactive decision making. However, approaching such systems is still too complex due to the paradigm shift they require, i.e., moving from scalable batch processing to continuous analysis and detection. Initially, modern big stream processing systems (e...
Chapter
Nowadays, modern Big Stream Processing Solutions (e.g. Spark, Flink) are working towards ultimate frameworks for streaming analytics. In order to achieve this goal, they started to offer extensions of SQL that incorporate stream-oriented primitives such as windowing and Complex Event Processing (CEP). The former enables stateful computation on infi...
Conference Paper
Full-text available
Data streams are increasingly needed for different types of applications and domains, where dynamicity and data velocity are of foremost importance. In this context, research challenges raise regarding the generation, publication, processing, and discovery of these streams, especially in distributed, heterogeneous and collaborative environments suc...
Conference Paper
Full-text available
It is a streaming world: a new generation of Web Applications is pushing the Web infrastructure to evolve and process data as soon as they arrive. However, the Web of Data is not appealing to the growing number of Web Applications demanding to tame Data Velocity. To solve these issues, we need to introduce new key abstractions, i.e., stream and eve...
Conference Paper
This half-day tutorial provides a comprehensive introduction to web stream processing, including the fundamental stream reasoning concepts, as well as an introduction to practical implementations and how to use them in concrete web applications. To this extent, we intend to (1) survey existing research outcomes from Stream Reasoning / RDF Stream Pr...
Article
Full-text available
In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current approaches c...
Article
Full-text available
rdf Stream Processing (rsp) is a rapidly evolving area of research that focuses on extensions of the Semantic Web in order to model and process Web data streams. While state-of-the-art approaches concentrate on server-side processing of rdf streams, we investigate the Triple Pattern Fragments Query Streamer (tpf-qs) method for server-side publishin...
Conference Paper
The RDF Stream Processing (RSP) is gaining momentum. The RDF stream data model is progressively adopted and many SPARQL extensions for continuous querying are converging to a unified RSP query language. However, the RSP community still has to investigate when transforming data streams in RDF streams pays off. In this paper, we report on several exp...
Conference Paper
Full-text available
In Stream Reasoning (SR), empirical research on RDF Stream Processing (RSP) is attracting a growing attention. The SR community proposed methodologies and benchmarks to investigate the RSP solution space and improve existing approaches. In this paper, we present RSPLab, an infrastructure that reduces the effort required to design and execute reprod...
Article
Full-text available
The ability to process large volumes of data on the fly, as soon as they become available, is a fundamental requirement in today’s information systems. Modern distributed stream processing engines (SPEs) address this requirement and provide low-latency and high-throughput data stream processing in cluster platforms, offering high-level programming...
Conference Paper
Full-text available
The rapid change and heterogeneity of today’s generated data calls for real-time decision making systems that can cope with the presented heterogeneity. In this paper, we present an Ontology Based Event Processing system that bridges the gap between ontology-based reasoning and event processing. We propose both a language and an architecture to per...
Conference Paper
Full-text available
Benchmarks like LSBench, SRBench, CSRBench and, more recently, CityBench satisfy the growing need of shared datasets, ontologies and queries to evaluate window-based RDF Stream Processing (RSP) engines. However, no clear winner emerges out of the evaluation. In this paper, we claim that the RSP community needs to adopt a Systematic Comparative Rese...

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