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NoSQL Data Models

NoSQL Data Models

By : Olivier Pivert
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NoSQL Data Models

NoSQL Data Models

By: Olivier Pivert

Overview of this book

Big Data environments are now to be handled in most current applications, this book addresses the latest issues and hurdles that are encountered in such environments. The book begins by presenting an overview of NoSQL languages and systems. Then, you’ll evaluate SPARQL queries over large RDF datasets and devise a solution that will use the MapReduce framework to process SPARQL graph patterns. Next, you’ll handle the production of web data, generate a set of links between two different datasets and overcome different heterogeneity problems. Moving ahead, you’ll take the multi-graph based approach to overcome challenges faced by the RDF data management community. Finally, you’ll deal with the flexible querying of graph databases and textual data management. By the end of this book, you’ll have gathered essential information on big data challenges faced by NoSQL databases.
Table of Contents (11 chapters)
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Preface
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Preface
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1
1 NoSQL Languages and Systems
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1.1. Introduction
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1.2. Join implementations on top of MapReduce
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1.3. Models for NoSQL languages and systems
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1.4. New challenges for database research
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1.5. Bibliography
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2 Distributed SPARQL Query Processing: a Case Study with Apache Spark
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2.1. Introduction
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2.2. RDF and SPARQL
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2.3. SPARQL query processing
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2.4. SPARQL and MapReduce
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2.5. SPARQL on Apache Spark
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2.6. Bibliography
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3 Doing Web Data: from Dataset Recommendation to Data Linking
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3.1. Introduction
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3.2. Datasets recommendation for data linking
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3.3. Challenges of linking data
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3.4. Techniques applied to the data linking process
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3.5. Conclusion
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3.6. Bibliography
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4 Big Data Integration in Cloud Environments: Requirements, Solutions and Challenges
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4.1. Introduction
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4.2. Big Data integration requirements in Cloud environments
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4.3. Automatic data store selection and discovery
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4.4. Unique access for all data stores
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4.5. Unified data model and query languages
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4.6. Query processing and optimization
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4.7. Summary and open issues
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4.8. Conclusion
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4.9. Bibliography
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5 Querying RDF Data: a Multigraph-based Approach
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5.1. Introduction
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5.2. Related work
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5.3. Background and preliminaries
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5.4. AMBER: a SPARQL querying engine
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5.5. Index construction
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5.6. Query matching procedure
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5.7. Experimental analysis
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5.8. Conclusion
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5.9. Acknowledgment
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5.10. Bibliography
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6 Fuzzy Preference Queries to NoSQL Graph Databases
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6.1. Introduction
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6.2. Preliminary statements
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6.3. Fuzzy preference queries over graph databases
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6.4. Implementation challenges
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6.5. Related work
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6.6. Conclusion and perspectives
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6.7. Acknowledgment
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6.8. Bibliography
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7 Relevant Filtering in a Distributed Content-based Publish/Subscribe System
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7.1. Introduction
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7.2. Related work: novelty and diversity filtering
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7.3. A Publish/Subscribe data model
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7.4. Publish/Subscribe relevance
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7.5. Real-time integration of novelty and diversity
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7.6. TDV updates
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7.7. Experiments
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7.8. Conclusion
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7.9. Bibliography
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List of Authors
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List of Authors
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Index
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Index
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End User License Agreement
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End User License Agreement

5.2. Related work

In order to efficiently answer SPARQL queries, many stores and APIs inspired by the relational model were proposed [ERL 12, BRO 02, NEU 10, CAR 04]. x-RDF-3X [NEU 10], inspired by modern RDBMS, representing RDF triples as a large three-attribute table. The RDF query processing is boosted using an exhaustive indexing schema coupled with statistics over the data. Also, Virtuoso [ERL 12] strongly exploits the RDBMS mechanism in order to answer SPARQL queries. Virtuoso is a column-store based system that uses sorted multi-column column-wise compressed projections. Furthermore, these systems build table indexing using standard B-trees. Jena [CAR 04] supplies API for manipulating RDF graphs. Jena exploits multiple-property tables that permit multiple views of graphs and vertices, which can be used simultaneously.

The database community has recently started to investigate RDF stores based on graph data management techniques [DAS 14, ZOU 14b, KIM 15]. The work in [DAS 14] addresses...

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