Graphs are a powerful tool for representing and understanding objects and their relationships in various application domains. The growing popularity of graph databases has generated data management problems that include finding efficient techniques for compressing large graph databases and suitable techniques for visualizing, browsing, and navigating large graph databases. Graph Data Management: Techniques and Applications is a central reference source for different data management techniques for graph data structures and their application. This book discusses graphs for modeling complex structured and schemaless data from the Semantic Web, social networks, protein networks, chemical compounds, and multimedia databases and offers essential research for academics working in the interdisciplinary domains of databases, data mining, and multimedia technology.
Author(s): Sherif Sakr
Edition: 1
Publisher: IGI Global
Year: 2011
Language: English
Pages: 502
Tags: Информатика и вычислительная техника;Компьютерная графика;
Title......Page 2
Copyright Page......Page 3
Editorial Advisory Board......Page 4
Table of Contents......Page 5
Forward......Page 8
Preface......Page 10
Acknowledgement......Page 11
Section 1. Basic Challenges of Data Management in Graph Databases
......Page 12
Graph Representation......Page 14
The Graph Traversal Pattern......Page 42
Data, Storage and Index Models for Graph Databases......Page 60
An Overview of Graph Indexing and Querying Techniques......Page 84
Efficient Techniques for Graph Searching and Biological Network Mining......Page 102
A Survey of Relational Approaches for Graph Pattern Matching over Large Graphs......Page 125
Labelling-Scheme-Based Subgraph Query Processing on Graph Data......Page 155
Section 2. Advanced Querying and Mining Aspects of Graph Databases
......Page 188
G-Hash......Page 189
TEDI......Page 227
Graph Mining Techniques......Page 252
Matrix Decomposition-Based Dimensionality Reduction on Graph Data......Page 273
Clustering Vertices in Weighted Graphs......Page 298
Large Scale Graph Mining with MapReduce......Page 312
Graph Representation and Anonymization in Large Survey Rating Data......Page 328
Section 3. Graph Database Applications in Various Domains
......Page 347
Querying RDF Data......Page 348
On the Efficiency of Querying and Storing RDF Documents......Page 367
Graph Applications in Chemoinformatics and Structural Bioinformatics......Page 399
Business Process Graphs......Page 434
A Graph-Based Approach for Semantic Process Model Discovery......Page 451
Shortest Path in Transportation Network and Weighted Subdivisions......Page 476
About the Contributors......Page 488
Index......Page 498
