Conference Paper

Big Data and Knowledge Management: How to Implement Conceptual Models in NoSQL Systems?

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... Only six studies are adjusted to a standard such as the Unified Modeling Language (UML) and the other four propose the use of their own modeling language, like Chebokto diagrams, Graph Object Oriented Semi-Structured Data Model (GOOSDM), lightweight metamodel extensions and XML. Of the six studies that present their models with the use of the UML, two of them use it in the conceptual level model [35,45], one uses it in the conceptual and logical models [14] and three use it in all conceptual, logical and physical levels [32,34,42]. In general, the authors propose the below algorithm that takes a model as input, apply their own transformation rules and produce another model as output: ...
... Only six studies are adjusted to a standard such as the Unified Modeling Language (UML) and the other four propose the use of their own modeling language, like Chebokto diagrams, Graph Object Oriented Semi-Structured Data Model (GOOSDM), lightweight metamodel extensions and XML. Of the six studies that present their models with the use of the UML, two of them use it in the conceptual level model [35,45], one uses it in the conceptual and logical models [14] and three use it in all conceptual, logical and physical levels [32,34,42]. ...
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The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. This study answers three research questions. The first question is how the number of published papers about Big Data modeling and management has evolved over time. The second question is whether the research is focused on semi-structured and/or unstructured data and what techniques are applied. Finally, the third question determines what trends and gaps exist according to three key concepts: the data source, the modeling and the database. As result, 36 studies, collected from the most important scientific digital libraries and covering the period between 2010 and 2019, were deemed relevant. Moreover, we present a complete bibliometric analysis in order to provide detailed information about the authors and the publication data in a single document. This SLR reveal very interesting facts. For instance, Entity Relationship and document-oriented are the most researched models at the conceptual and logical abstraction level respectively and MongoDB is the most frequent implementation at the physical. Furthermore, 2.78% studies have proposed approaches oriented to hybrid databases with a real case for structured, semi-structured and unstructured data.
... The purpose of the work [10] presented by Abdelhedi et al. is to implement a conceptual model describing Big Data into NoSQL database and they choose to focus on column-oriented NoSQL model. This paper aims to rethink and to complete the work presented by Abdelhedi et al. [6,10], by applying the standard MOF 2.0 QVT and Acceleo to develop the transformation rules aiming at automatically generating the creation code of column-oriented NoSQL database. ...
... The purpose of the work [10] presented by Abdelhedi et al. is to implement a conceptual model describing Big Data into NoSQL database and they choose to focus on column-oriented NoSQL model. This paper aims to rethink and to complete the work presented by Abdelhedi et al. [6,10], by applying the standard MOF 2.0 QVT and Acceleo to develop the transformation rules aiming at automatically generating the creation code of column-oriented NoSQL database. It is actually the only work for reaching this goal. ...
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span>The growth of application architectures in all areas (e.g. Astrology, Meteorology, E-commerce, social network, etc.) has resulted in an exponential increase in data volumes, now measured in Petabytes. Managing these volumes of data has become a problem that relational databases are no longer able to handle because of the acidity properties. In response to this scaling up, new concepts have emerged such as NoSQL. In this paper, we show how to design and apply transformation rules to migrate from an SQL relational database to a Big Data solution within NoSQL. For this, we use the Model Driven Architecture (MDA) and the transformation languages like as MOF 2.0 QVT (Meta-Object Facility 2.0 Query-View-Transformation) and Acceleo which define the meta-models for the development of transformation model. The transformation rules defined in this work can generate, from the class diagram, a CQL code for creation column-oriented NoSQL database.</span
... Les travaux de ce chapitre ont été présentés dans les publications suivantes : [Abdelhedi et al., 2016a], [Abdelhedi et al., 2016b], [Abdelhedi et al., 2016c], [Abdelhedi et al., 2017a], [Abdelhedi et al., 2017b], [Abdelhedi et al., 2017c], [Abdelhedi et al., 2018a] et [Abdelhedi et al., 2018b]. ...
Thesis
It is widely accepted today that relational systems are not appropriate to handle Big Data. This has led to a new category of databases commonly known as NoSQL databases that were created in response to the needs for better scalability, higher flexibility and faster data access. These systems have proven their efficiency to store and query Big Data. Unfortunately, only few works have presented approaches to implement conceptual models describing Big Da-ta in NoSQL systems. This paper proposes an automatic MDA-based approach that provides a set of transformations, formalized with the QVT language, to translate UML conceptual models into NoSQL models. In our approach, we build an intermediate logical model compatible with column, document, graph and key-value systems. The advantage of using a unified logical model is that this model remains stable, even though the NoSQL system evolves over time which simplifies the transformation process and saves developers efforts and time.
... The importance of this study is to regain for new research areas related to knowledge management system that is previously dominated by the lucrative KMS implementation topics 11,12 , KMS usage topics 13,14 , KMS security 15,16 , KMS performance 17,18 and the use of new technology to apply KMS in an organization 8,13 . Whereas, the numerous new topics arise recently which are related to knowledge management system, such as the process of handling big data phenomena [19][20][21] , the implementation of KMS in social media and complex environment 22,23 , and gathering information effectively 24 . ...
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Nowadays, the number of papers on the topic of Knowledge Management and Knowledge Management System is still widely discussed. The study of Knowledge Management System (KMS) issues are based on Systematic Literature Review (SLR). It aims to analyze the state of the art, identify current popular issues on KMS, and offer directions for future research agenda. The methodology used in this paper is based on the systematic literature review to collect, synthesize and analyze numerous papers on a variety of topics that are closely related to knowledge management system issues that published in the last two decades. Based on fifty-four papers reviewed from six electronic databases, the result of this paper obtained fourteen current issues on knowledge management system. Moreover, the top three popular issues consist of the development of capabilities and features of KMS, Big Data issues on KMS, and adoption to new technology issue for KMS respectively. The conclusion of this study emphasized the big data phenomenon as the most contemporary topic for the future research area besides the growing of required KMS capabilities and features development.
Article
This paper presents the implementation of a knowledge base supporting an intelligent system to solve problems of optimization especially problems of discrete production processes optimization called Intelligent Algebraic-Logical Meta-Model (ALMM) Solver. Using a unified description of selected optimization problems, an ontological knowledge base was designed, which allows for selective selection of Intelligent ALMM Solver components necessary to solve and model problems. Using the definitions of the properties of optimization problems, scalable components describing exemplary optimization jobs were selected. Ontology for this area was developed, with particular emphasis on the requirements of the ALMM Solver. Using the possibility of interactive communication with the ALMM ontology in the form of SQL queries in the experimental part of the work, exemplary queries for the designed Knowledge Base (KB) module were presented, and the response generated by the system is a scenario of intelligent selection of a set of components modeling and solving a given problem. Such an innovative approach allows for dynamic construction of algorithms solving problems of discrete optimization. The use of knowledge about the properties of the considered processes and ALMM technology universalizes the proposed KB system making it an intelligent and efficient tool for solving discrete optimization jobs. The key advantage of the proposed ontological approach is the ability to flexibly expand it and extend its use to other classes of problems which have already been described in the ALMM technology.
Research Proposal
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Chapter
Typically, to implement a data warehouse, we have to extract the data from relational databases, XML files, etc., which are very used by companies. Since today’s data are generated from social media, GPS data, sensor data, surveillance data, etc., which are maintained in NoSQL databases, we are talking about big data warehouses (BDW). Hence, there is a need to study the influence of this new paradigm in the creation of the data warehouse (DW) and the ETL process (Big ETL). This paper presents an overview of the work dealing with proposals in this context .
Conference Paper
Big Data has been described as a four-dimensional model with Volume, Variety, Velocity, and Veracity. In this paper we discuss the potential of a model-driven approach (MDA) to tackle design issues of Big Data taking into account the effect of the four dimensions. Our approach considers NoSQL graph databases. The approach is applied to the case of Neo4j database. Our main contribution is an MDA methodology that enables to tackle the four V’s dimensions described above. It consists of two major steps: (i) a forward engineering approach based on MDA as well as a set of transformations rules enabling the development of a conceptual, logical, and physical model for big data encompassing the four V’s, (ii) a volume-guided approach supporting the generation of test bases dedicated to performance evaluation. We present an illustrative scenario of our forward engineering approach.
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