NoSQL Data Models : Trends and Challenges.

Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- 1. NoSQL Languages and Systems -- 1.1. Introduction -- 1.1.1. The rise of NoSQL systems and languages -- 1.1.2. Overview of NoSQL concepts -- 1.1.3. Current trends of French research in NoSQL languages -- 1.2. Join implementations on top of MapReduce -- 1.3. Models for NoSQL languages and systems -- 1.4. New challenges for database research -- 1.5. Bibliography -- 2. Distributed SPARQL Query Processing: a Case Study with Apache Spark -- 2.1. Introduction -- 2.2. RDF and SPARQL -- 2.2.1. RDF framework and data model -- 2.2.2. SPARQL query language -- 2.3. SPARQL query processing -- 2.3.1. SPARQL with and without RDF/Sentailment -- 2.3.2. Query optimization -- 2.3.3. Triple store systems -- 2.4. SPARQL and MapReduce -- 2.4.1. MapReduce-based SPARQL processing -- 2.4.2. Related work -- 2.5. SPARQL on Apache Spark -- 2.5.1. Apache Spark -- 2.5.2. SPARQL on Spark -- 2.5.3. Experimental evaluation -- 2.6. Bibliography -- 3. Doing Web Data: from Dataset Recommendation to Data Linking -- 3.1. Introduction -- 3.1.1. The Semantic Web vision -- 3.1.2. Linked data life cycles -- 3.1.3. Chapter overview -- 3.2. Datasets recommendation for data linking -- 3.2.1. Process definition -- 3.2.2. Dataset recommendation for data linking based on a Semantic Web index -- 3.2.3. Dataset recommendation for data linking based on socialnetworks -- 3.2.4. Dataset recommendation for data linking based on domain specific keywords -- 3.2.5. Dataset recommendation for data linking based on topicm odeling -- 3.2.6. Dataset recommendation for data linking based on topic profiles -- 3.2.7. Dataset recommendation for data linking based on intensional profiling -- 3.2.8. Discussion on dataset recommendation approaches -- 3.3. Challenges of linking data -- 3.3.1. Value dimension.

3.3.2. Ontological dimension -- 3.3.3. Logical dimension -- 3.4. Techniques applied to the data linking process -- 3.4.1. Data linking techniques -- 3.4.2. Discussion -- 3.5. Conclusion -- 3.6. Bibliography -- 4. Big Data Integration in Cloud Environments: Requirements, Solutions and Challenges -- 4.1. Introduction -- 4.2. Big Data integration requirements in Cloud environments -- 4.3. Automatic data store selection and discovery -- 4.3.1. Introduction -- 4.3.2. Model-based approaches -- 4.3.3. Matching-oriented approaches -- 4.3.4. Comparison -- 4.4. Unique access for all data stores -- 4.4.1. Introduction -- 4.4.2. ODBAPI: a unified REST API for relational and NoSQL data stores -- 4.4.3. Other works -- 4.4.4. Comparison -- 4.5. Unified data model and query languages -- 4.5.1. Introduction -- 4.5.2. Data models of classical data integration approaches -- 4.5.3. A global schema to unify the view over relational and NoSQL data stores -- 4.5.4. Other works -- 4.5.5. Comparison -- 4.6. Query processing and optimization -- 4.6.1. Introduction -- 4.6.2. Federated query language approaches -- 4.6.3. Integrated query language approaches -- 4.6.4. Comparison -- 4.7. Summary and open issues -- 4.7.1. Summary -- 4.7.2. Open issues -- 4.8. Conclusion -- 4.9. Bibliography -- 5. Querying RDF Data: a Multigraph-based Approach -- 5.1. Introduction -- 5.2. Related work -- 5.3. Background and preliminaries -- 5.3.1. RDF data -- 5.3.2. SPARQL query -- 5.3.3. SPARQL querying by adopting multigraph homomorphism -- 5.4. AMBER: a SPARQL querying engine -- 5.5. Index construction -- 5.5.1. Attribute index -- 5.5.2. Vertex signature index -- 5.5.3. Vertex neighborhood index -- 5.6. Query matching procedure -- 5.6.1. Vertex-level processing -- 5.6.2. Processing satellite vertices -- 5.6.3. Arbitrary query processing -- 5.7. Experimental analysis -- 5.7.1. Experimental setu.

5.7.2. Workload generation -- 5.7.3. Comparison with RDF engines -- 5.8. Conclusion -- 5.9. Acknowledgment -- 5.10. Bibliography -- 6. Fuzzy Preference Queries to NoSQL Graph Databases -- 6.1. Introduction -- 6.2. Preliminary statements -- 6.2.1. Graph databases -- 6.2.2. Fuzzy set theory -- 6.3. Fuzzy preference queries over graph databases -- 6.3.1. Fuzzy preference queries over crisp graph databases -- 6.3.2. Fuzzy preference queries over fuzzy graph databases -- 6.4. Implementation challenges -- 6.4.1. Modeling fuzzy databases -- 6.4.2. Evaluation of queries with fuzzy preferences -- 6.4.3. Scalability -- 6.5. Related work -- 6.6. Conclusion and perspectives -- 6.7. Acknowledgment -- 6.8. Bibliography -- 7. Relevant Filtering in a Distributed Content-based Publish/Subscribe System -- 7.1. Introduction -- 7.2. Related work: novelty and diversity filtering -- 7.3. A Publish/Subscribe data model -- 7.3.1. Data model -- 7.3.2. Weighting terms in textual data flows -- 7.4. Publish/Subscribe relevance -- 7.4.1. Items and histories -- 7.4.2. Novelty -- 7.4.3. Diversity -- 7.4.4. An overview of the filtering process -- 7.4.5. Choices of relevance -- 7.5. Real-time integration of novelty and diversity -- 7.5.1. Centralized implementation -- 7.5.2. Distributed filtering -- 7.6. TDV updates -- 7.6.1. TDV computation techniques -- 7.6.2. Incremental approach -- 7.6.3. TDV in a distributed environment -- 7.7. Experiments -- 7.7.1. Implementation and description of datasets -- 7.7.2. TDV updates -- 7.7.3. Filtering rate -- 7.7.4. Performance evaluation in the centralized environment -- 7.7.5. Performance evaluation in a distributed environment -- 7.7.6. Quality of filtering -- 7.8. Conclusion -- 7.9. Bibliography -- List of Authors -- Index -- Other titles from iSTE in Computer Engineering -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.