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  • Book Overview & Buying NoSQL Data Models
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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
Lock Free Chapter
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

2.1. Introduction

The Semantic Web is rapidly growing, generating large volumes of Resource Description Framework (RDF) data [W3C 14] stored in the Linked Open Data (LOD) cloud. With data sets ranging from hundreds of millions to billions of triples, RDF triple stores are expected to meet properties such as scalability, high availability, automatic work distribution and fault tolerance. This chapter is dedicated to the problem of evaluating SPARQL queries over large RDF datasets. Section 2.2 introduces the RDF data model and the SPARQL query language. The challenges and solutions for efficiently processing SPARQL queries and in particular basic graph pattern (BGP) expressions are presented in section 2.3. The specific solution using the MapReduce framework for processing SPARQL graph patterns [DEA 04] is introduced in section 2.4. The chapter concludes with section 2.5, describing the use of Apache Spark and explaining the importance of the physical data layers for the query performance...

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