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E-raamat: NoSQL Data Models - Trends and Challenges: Trends and Challenges [Wiley Online]

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  • Formaat: 288 pages
  • Ilmumisaeg: 10-Jul-2018
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119528224
  • ISBN-13: 9781119528227
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  • Wiley Online
  • Hind: 174,45 €*
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NoSQL Data Models - Trends and Challenges: Trends and ChallengesSuurem pilt
  • Formaat: 288 pages
  • Ilmumisaeg: 10-Jul-2018
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119528224
  • ISBN-13: 9781119528227
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119528227

The topic of NoSQL databases has recently emerged, to face the Big Data challenge, namely the ever increasing volume of data to be handled. It is now recognized that relational databases are not appropriate in this context, implying that new database models and techniques are needed. This book presents recent research works, covering the following basic aspects: semantic data management, graph databases, and big data management in cloud environments. The chapters in this book report on research about the evolution of basic concepts such as data models, query languages, and new challenges regarding implementation issues.

Foreword xi
Anne Laurent
Dominique Laurent
Preface xiii
Olivier Pivert
Chapter 1 NoSQL Languages and Systems 1(20)
Kim Nguyen
1.1 Introduction
1(6)
1.1.1 The rise of NoSQL systems and languages
1(3)
1.1.2 Overview of NoSQL concepts
4(2)
1.1.3 Current trends of French research in NoSQL languages
6(1)
1.2 Join implementations on top of MapReduce
7(5)
1.3 Models for NoSQL languages and systems
12(4)
1.4 New challenges for database research
16(2)
1.5 Bibliography
18(3)
Chapter 2 Distributed SPARQL Query Processing: a Case Study with Apache Spark 21(36)
Bernd Amann
Olivier Cure
Hubert Naacke
2.1 Introduction
21(1)
2.2 RDF and SPARQL
22(7)
2.2.1 RDF framework and data model
22(3)
2.2.2 SPARQL query language
25(4)
2.3 SPARQL query processing
29(5)
2.3.1 SPARQL with and without RDF/S entailment
29(1)
2.3.2 Query optimization
30(3)
2.3.3 Triple store systems
33(1)
2.4 SPARQL and MapReduce
34(7)
2.4.1 MapReduce-based SPARQL processing
35(4)
2.4.2 Related work
39(2)
2.5 SPARQL on Apache Spark
41(12)
2.5.1 Apache Spark
41(1)
2.5.2 SPARQL on Spark
42(6)
2.5.3 Experimental evaluation
48(5)
2.6 Bibliography
53(4)
Chapter 3 Doing Web Data: from Dataset Recommendation to Data Linking 57(36)
Manel Achichi
Mohamed Ben Ellefi
Zohra Bellahsene
Konstantin Todorov
3.1 Introduction
57(5)
3.1.1 The Semantic Web vision
57(1)
3.1.2 Linked data life cycles
58(3)
3.1.3
Chapter overview
61(1)
3.2 Datasets recommendation for data linking
62(7)
3.2.1 Process definition
63(1)
3.2.2 Dataset recommendation for data linking based on a Semantic Web index
64(1)
3.2.3 Dataset recommendation for data linking based on social networks
64(1)
3.2.4 Dataset recommendation for data linking based on domain-specific keywords
65(1)
3.2.5 Dataset recommendation for data linking based on topic modeling
65(1)
3.2.6 Dataset recommendation for data linking based on topic profiles
66(1)
3.2.7 Dataset recommendation for data linking based on intensional profiling
67(1)
3.2.8 Discussion on dataset recommendation approaches
68(1)
3.3 Challenges of linking data
69(9)
3.3.1 Value dimension
70(4)
3.3.2 Ontological dimension
74(3)
3.3.3 Logical dimension
77(1)
3.4 Techniques applied to the data linking process
78(8)
3.4.1 Data linking techniques
79(4)
3.4.2 Discussion
83(3)
3.5 Conclusion
86(1)
3.6 Bibliography
87(6)
Chapter 4 Big Data Integration in Cloud Environments: Requirements, Solutions and Challenges 93(42)
Rami Sellami
Bruno Defude
4.1 Introduction
93(3)
4.2 Big Data integration requirements in Cloud environments
96(3)
4.3 Automatic data store selection and discovery
99(4)
4.3.1 Introduction
99(1)
4.3.2 Model-based approaches
99(1)
4.3.3 Matching-oriented approaches
100(2)
4.3.4 Comparison
102(1)
4.4 Unique access for all data stores
103(5)
4.4.1 Introduction
103(1)
4.4.2 ODBAPI: a unified REST API for relational and NoSQL data stores
104(1)
4.4.3 Other works
105(2)
4.4.4 Comparison
107(1)
4.5 Unified data model and query languages
108(10)
4.5.1 Introduction
108(1)
4.5.2 Data models of classical data integration approaches
109(1)
4.5.3 A global schema to unify the view over relational and NoSQL data stores
110(3)
4.5.4 Other works
113(4)
4.5.5 Comparison
117(1)
4.6 Query processing and optimization
118(7)
4.6.1 Introduction
118(1)
4.6.2 Federated query language approaches
118(3)
4.6.3 Integrated query language approaches
121(3)
4.6.4 Comparison
124(1)
4.7 Summary and open issues
125(4)
4.7.1 Summary
125(2)
4.7.2 Open issues
127(2)
4.8 Conclusion
129(1)
4.9 Bibliography
129(6)
Chapter 5 Querying RDF Data: a Multigraph-based Approach 135(32)
Vijay Ingalalli
Dino Ienco
Pascal Poncelet
5.1 Introduction
135(2)
5.2 Related work
137(1)
5.3 Background and preliminaries
137(6)
5.3.1 RDF data
138(2)
5.3.2 SPARQL query
140(2)
5.3.3 SPARQL querying by adopting multigraph homomorphism
142(1)
5.4 AMBER: a SPARQL querying engine
143(1)
5.5 Index construction
144(5)
5.5.1 Attribute index
144(1)
5.5.2 Vertex signature index
145(3)
5.5.3 Vertex neighborhood index
148(1)
5.6 Query matching procedure
149(10)
5.6.1 Vertex-level processing
151(1)
5.6.2 Processing satellite vertices
152(2)
5.6.3 Arbitrary query processing
154(5)
5.7 Experimental analysis
159(5)
5.7.1 Experimental setup
159(1)
5.7.2 Workload generation
160(1)
5.7.3 Comparison with RDF engines
161(3)
5.8 Conclusion
164(1)
5.9 Acknowledgment
164(1)
5.10 Bibliography
164(3)
Chapter 6 Fuzzy Preference Queries to NoSQL Graph Databases 167(36)
Arnaud Castelltort
Anne Laurent
Olivier Pivert
Olfa Slama
Virginie Thion
6.1 Introduction
167(1)
6.2 Preliminary statements
168(8)
6.2.1 Graph databases
168(6)
6.2.2 Fuzzy set theory
174(2)
6.3 Fuzzy preference queries over graph databases
176(17)
6.3.1 Fuzzy preference queries over crisp graph databases
176(6)
6.3.2 Fuzzy preference queries over fuzzy graph databases
182(11)
6.4 Implementation challenges
193(4)
6.4.1 Modeling fuzzy databases
193(1)
6.4.2 Evaluation of queries with fuzzy preferences
193(2)
6.4.3 Scalability
195(2)
6.5 Related work
197(1)
6.6 Conclusion and perspectives
198(1)
6.7 Acknowledgment
199(1)
6.8 Bibliography
199(4)
Chapter 7 Relevant Filtering in a Distributed Content-based Publish/Subscribe System 203(42)
Cedric Du Mouza
Nicolas Travers
7.1 Introduction
203(2)
7.2 Related work: novelty and diversity filtering
205(1)
7.3 A Publish/Subscribe data model
206(2)
7.3.1 Data model
206(1)
7.3.2 Weighting terms in textual data flows
207(1)
7.4 Publish/Subscribe relevance
208(4)
7.4.1 Items and histories
208(1)
7.4.2 Novelty
209(1)
7.4.3 Diversity
209(1)
7.4.4 An overview of the filtering process
210(1)
7.4.5 Choices of relevance
210(2)
7.5 Real-time integration of novelty and diversity
212(9)
7.5.1 Centralized implementation
212(4)
7.5.2 Distributed filtering
216(5)
7.6 TDV updates
221(7)
7.6.1 TDV computation techniques
221(2)
7.6.2 Incremental approach
223(2)
7.6.3 TDV in a distributed environment
225(3)
7.7 Experiments
228(13)
7.7.1 Implementation and description of datasets
229(1)
7.7.2 TDV updates
229(1)
7.7.3 Filtering rate
230(4)
7.7.4 Performance evaluation in the centralized environment
234(4)
7.7.5 Performance evaluation in a distributed environment
238(2)
7.7.6 Quality of filtering
240(1)
7.8 Conclusion
241(1)
7.9 Bibliography
242(3)
List of Authors 245(2)
Index 247
Olivier Pivert is currently a full Professor of Computer Science at the National School of Applied Sciences and Technology, Lannion, France; and a Member of the Institute for Research in Computer Science and Random Systems where he heads the Shaman research team.
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