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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

7.7. Experiments

In this section, we study the behavior of TDV computation, the filtering system in both centralized and NoSQL environments. We will also show the impact of several parameters (i.e. novelty threshold, diversity and size of the sliding window) with a real dataset of items. Finally, thanks to a user validation, we study the quality of our system with different settings and a periodic filtering based on a top-k approach.

7.7.1. Implementation and description of datasets

For our experiments, we used a subset from a real dataset of items acquired over an 8-month campaign from March to October 2010 [TRA 14]. Subscriptions were generated by using the ALIAS sampling method [WAL 77]. It produced 10M subscriptions that follow the distribution of term occurrences on the Web, and the Web query size reported in [BEI 04], based on the vocabulary of 1.5M distinct terms extracted from items. It is characterized among others by a maximum size equal to 12 terms and on average 2.2 terms...

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