Book Image

NoSQL Data Models

By : Olivier Pivert
Book Image

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.

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NoSQL Data Models
Olivier Pivert
Aug, 2018 | 278 pages
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Table of Contents (11 chapters)
Preface
Preface
Free Chapter
1
1 NoSQL Languages and Systems
1.1. Introduction
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
2 Distributed SPARQL Query Processing: a Case Study with Apache Spark
2.1. Introduction
2.2. RDF and SPARQL
2.3. SPARQL query processing
2.4. SPARQL and MapReduce
2.5. SPARQL on Apache Spark
2.6. Bibliography
3
3 Doing Web Data: from Dataset Recommendation to Data Linking
3.1. Introduction
3.2. Datasets recommendation for data linking
3.3. Challenges of linking data
3.4. Techniques applied to the data linking process
3.5. Conclusion
3.6. Bibliography
4
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
4.4. Unique access for all data stores
4.5. Unified data model and query languages
4.6. Query processing and optimization
4.7. Summary and open issues
4.8. Conclusion
4.9. Bibliography
5
5 Querying RDF Data: a Multigraph-based Approach
5.1. Introduction
5.2. Related work
5.3. Background and preliminaries
5.4. AMBER: a SPARQL querying engine
5.5. Index construction
5.6. Query matching procedure
5.7. Experimental analysis
5.8. Conclusion
5.9. Acknowledgment
5.10. Bibliography
6
6 Fuzzy Preference Queries to NoSQL Graph Databases
6.1. Introduction
6.2. Preliminary statements
6.3. Fuzzy preference queries over graph databases
6.4. Implementation challenges
6.5. Related work
6.6. Conclusion and perspectives
6.7. Acknowledgment
6.8. Bibliography
7
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
7.4. Publish/Subscribe relevance
7.5. Real-time integration of novelty and diversity
7.6. TDV updates
7.7. Experiments
7.8. Conclusion
7.9. Bibliography
8
List of Authors
List of Authors
9
Index
Index
10
End User License Agreement
End User License Agreement

2.3. SPARQL query processing

RDF has become a de facto standard for publishing information and knowledge on the Web. Compared to other standards such as XML or JSON, RDF facilitates in particular the integration of information by linking RDF resources of separate data sets through owl:sameAs, rdf:type, and rdfs:subClassOf properties. The resulting Semantic Web of linked open data (LOD) sets is composed of billions of triples, and building systems for efficiently storing and querying the Semantic Web is a technological and scientific challenge reminiscent of other Big Data applications.

2.3.1. SPARQL with and without RDF/S entailment

The first challenge concerns the interaction between SPARQL’s graph pattern matching semantics and RDF’s data model semantics defined by the RDF/S entailment rules. There mainly exist two solutions to evaluating a query Q over a data set D and a set of entailment rules R. The first solution is to first saturate D by recursively applying all...

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