Sakr / Wylot / Mutharaju Linked Data

Storing, Querying, and Reasoning
1. Auflage 2018
ISBN: 978-3-319-73515-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 236 Seiten

This book describes efficient and effective techniques for harnessing the power of Linked Data by tackling the various aspects of managing its growing volume: storing, querying, reasoning, provenance management and benchmarking.

To this end, Chapter 1 introduces the main concepts of the Semantic Web and Linked Data and provides a roadmap for the book. Next, Chapter 2 briefly presents the basic concepts underpinning Linked Data technologies that are discussed in the book. Chapter 3 then offers an overview of various techniques and systems for centrally querying RDF datasets, and Chapter 4 outlines various techniques and systems for efficiently querying large RDF datasets in distributed environments. Subsequently, Chapter 5 explores how streaming requirements are addressed in current, state-of-the-art RDF stream data processing. Chapter 6 covers performance and scaling issues of distributed RDF reasoning systems, while Chapter 7 details benchmarks for RDF query engines and instance matching systems. Chapter 8 addresses the provenance management for Linked Data and presents the different provenance models developed. Lastly, Chapter 9 offers a brief summary, highlighting and providing insights into some of the open challenges and research directions.
Providing an updated overview of methods, technologies and systems related to Linked Data this book is mainly intended for students and researchers who are interested in the Linked Data domain. It enables students to gain an understanding of the foundations and underpinning technologies and standards for Linked Data, while researchers benefit from the in-depth coverage of the emerging and ongoing advances in Linked Data storing, querying, reasoning, and provenance management systems. Further, it serves as a starting point to tackle the next research challenges in the domain of Linked Data management.

Sherif Sakr is a professor of computer and information science in the Health Informatics department at King Saud bin Abdulaziz University for Health Sciences, and is also affiliated with the University of New South Wales and DATA61/CSIRO in Australia. Sherif's research interests revolve around the areas of efficient and scalable Big Data Management, Processing and Analytics. In 2013, he was awarded the Stanford Innovation and Entrepreneurship Certificate.
Marcin Wylot is a postdoctoral researcher at TU Berlin, Germany, in the Open Distributed Systems group. His main research interests are in database systems for Semantic Web data, provenance in Linked Data, Internet of Things, and Big Data processing.
Raghava Mutharaju is a research scientist in the AI & Machine Learning Systems division of GE Global Research in Niskayuna, NY, USA. His research interests are in ontology modeling and reasoning, scalable SPARQL query processing, Big Data, Semantic Web and its applications.
Danh Le Phuoc is a Marie Sklodowaka-Curie Fellow at TU Berlin. He is working on Pervasive Analytics which includes Linked Data/Semantic Web, Pervasive Computing, Future Internet and Big Data for Internet of Everything.
Irini Fundulaki is a Principal Researcher at the Institute of Computer Science of the Foundation for Research and Technology-Hellas. Her research interests are related to Web Data Management and more specifically the development of benchmarks for RDF engines, instance matching and link discovery systems, and the management of provenance for Linked Data.

Autoren/Hrsg.

Sakr, Sherif

Wylot, Marcin

Mutharaju, Raghava

Le Phuoc, Danh

Fundulaki, Irini

Inhaltsverzeichnis

1;Foreword;6
2;Preface;8
2.1;Organization of the Book;9
2.2;Target Audience;10
3;Acknowledgments;12
4;Contents;13
5;About the Authors;16
6;1 Introduction;18
6.1;1.1 Semantic Web;18
6.2;1.2 Linked Data;22
6.3;1.3 Book Roadmap;24
7;2 Fundamentals;26
7.1;2.1 Linked Data;26
7.2;2.2 RDF;29
7.3;2.3 SPARQL;33
7.4;2.4 OWL;36
7.5;2.5 Reasoning;38
7.6;2.6 OWL 2 Profiles;42
7.7;2.7 Modern Big Data Storage and Processing Systems;43
7.7.1;2.7.1 NoSQL Databases;43
7.7.2;2.7.2 MapReduce/Hadoop;45
7.7.3;2.7.3 Spark;47
8;3 Centralized RDF Query Processing;50
8.1;3.1 RDF Statement Table;50
8.2;3.2 Index Permutations for RDF Triples;53
8.3;3.3 Property Tables;57
8.4;3.4 Vertical Partitioning;59
8.5;3.5 Graph-Based Storage;61
8.6;3.6 Binary Encoding for RDF Databases;65
9;4 Distributed RDF Query Processing;67
9.1;4.1 NoSQL-Based RDF Systems;67
9.2;4.2 Hadoop-Based RDF Systems;71
9.3;4.3 Spark-Based RDF Systems;77
9.4;4.4 Main Memory-Based Distributed Systems;79
9.5;4.5 Other Distributed RDF Systems;82
9.6;4.6 Federated RDF Query Processing;90
10;5 Processing of RDF Stream Data;100
10.1;5.1 RDF Streaming Data in A Nutshell;100
10.2;5.2 Data Representation of RDF Streams;103
10.3;5.3 RDF Streaming Query Model;105
10.3.1;5.3.1 Stream-to-Stream Operator;106
10.3.2;5.3.2 Stream-to-Relation Operator;106
10.3.3;5.3.3 Relation-to-Relation Operator;107
10.4;5.4 RDF Streaming Query Languages and Syntax;109
10.5;5.5 System Design and Implementation;111
10.5.1;5.5.1 Design;111
10.5.2;5.5.2 Implementation Aspects;113
10.5.2.1;5.5.2.1 Time Management;113
10.5.2.2;5.5.2.2 Scheduling and Handling Memory;115
10.5.3;5.5.3 Systems;116
10.5.3.1;5.5.3.1 Streaming SPARQL;117
10.5.3.2;5.5.3.2 C-SPARQL;118
10.5.3.3;5.5.3.3 EP-SPARQL;120
10.5.3.4;5.5.3.4 SPARQLstream;121
10.5.3.5;5.5.3.5 CQELS;121
11;6 Distributed Reasoning of RDF Data;124
11.1;6.1 The Process of RDF Reasoning;124
11.2;6.2 Peer-to-Peer RDF Reasoning Systems;127
11.3;6.3 NoSQL-Based RDF Reasoning Systems;131
11.4;6.4 Hadoop-Based RDF Reasoning Systems;132
11.5;6.5 Spark-Based RDF Reasoning Systems;135
11.6;6.6 Shared Memory RDF Reasoning Systems;137
11.7;6.7 Influence on Other Semantic Web Languages;139
12;7 Benchmarking RDF Query Engines and Instance Matching Systems;142
12.1;7.1 Benchmark Definition and Principles;142
12.1.1;7.1.1 Overview;142
12.1.2;7.1.2 Benchmark Development Methodology;144
12.1.3;7.1.3 Choke Points;145
12.2;7.2 Benchmarks for RDF Query Engines;147
12.2.1;7.2.1 Real Benchmarks;148
12.2.1.1;7.2.1.1 UniProt;148
12.2.1.2;7.2.1.2 YAGO (Yet Another Great Ontology);149
12.2.1.3;7.2.1.3 Barton Library;149
12.2.2;7.2.2 Synthetic RDF Benchmarks;152
12.2.2.1;7.2.2.1 Lehigh University Benchmark (LUBM);152
12.2.2.2;7.2.2.2 SP2Bench;154
12.2.2.3;7.2.2.3 Berlin SPARQL Benchmark (BSBM);156
12.2.2.4;7.2.2.4 Semantic Publishing Benchmark (SPB);161
12.2.3;7.2.3 Benchmark Generators;167
12.2.3.1;7.2.3.1 DBPedia SPARQL Benchmark (DBSB);167
12.2.3.2;7.2.3.2 Waterloo SPARQL Diversity Test Suite;169
12.2.3.3;7.2.3.3 FEASIBLE;171
12.2.4;7.2.4 Dataset Structuredness;172
12.3;7.3 Benchmarks for Instance Matching Systems;174
12.3.1;7.3.1 Datasets;176
12.3.2;7.3.2 Variations;176
12.3.3;7.3.3 Reference Alignment;177
12.3.4;7.3.4 Key Performance Indicators;178
12.3.5;7.3.5 Real Benchmarks;178
12.3.5.1;7.3.5.1 A-R-S 2009;178
12.3.5.2;7.3.5.2 Data Interlinking (DI) 2010;180
12.3.5.3;7.3.5.3 Data Interlinking (DI) 2011;181
12.3.5.4;7.3.5.4 Overall Evaluation of Real Benchmarks;181
12.3.6;7.3.6 Synthetic Benchmarks for Instance Matching Systems;182
12.3.6.1;7.3.6.1 IIMB 2009;182
12.3.6.2;7.3.6.2 IIMB 2010;184
12.3.6.3;7.3.6.3 Person-Restaurants (PR) 2010;187
12.3.6.4;7.3.6.4 IIMB 2011;188
12.3.6.5;7.3.6.5 Sandbox 2012;188
12.3.6.6;7.3.6.6 IIMB 2012;189
12.3.6.7;7.3.6.7 RDFT 2013;189
12.3.6.8;7.3.6.8 ID-REC 2014;190
12.3.6.9;7.3.6.9 SPIMBench 2015;190
12.3.6.10;7.3.6.10 ONTOlogy Matching Benchmark with Many Instances (ONTOBI);191
12.3.7;7.3.7 Overall Evaluation of Synthetic Benchmarks;192
12.4;7.4 Instance Matching Benchmark Generators for Linked Data;192
12.4.1;7.4.1 SWING;192
12.4.2;7.4.2 SPIMBENCH;193
12.4.3;7.4.3 LANCE;194
13;8 Provenance Management for Linked Data;195
13.1;8.1 An Overview of Provenance Models;195
13.2;8.2 Provenance Representations;197
13.3;8.3 Provenance Models;198
13.3.1;8.3.1 Relational Provenance;198
13.3.2;8.3.2 RDF Provenance;199
13.3.3;8.3.3 Update Provenance;202
13.4;8.4 Provenance in Data Management Systems;204
14;9 Conclusions and Outlook;210
14.1;9.1 Conclusions;210
14.2;9.2 Outlook;213
15;References;216