eBook Graph Data Management: Techniques and Applications, 1st Edition

  • Sherif Sakr
  • Published By:
  • ISBN-10: 1613500548
  • ISBN-13: 9781613500545
  • DDC: 001.4
  • Grade Level Range: College Freshman - College Senior
  • 502 Pages | eBook
  • Original Copyright 2012 | Published/Released November 2012
  • This publication's content originally published in print form: 2012
  • Price:  Sign in for price



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.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Advisory Board.
Table of Contents.
1: Basic Challenges of Data Management in Graph Databases.
2: Graph Representation.
3: The Graph Traversal Pattern.
4: Data, Storage and Index Models for Graph Databases.
5: An Overview of Graph Indexing and Querying Techniques.
6: Efficient Techniques for Graph Searching and Biological Network Mining.
7: A Survey of Relational Approaches for Graph Pattern Matching over Large Graphs.
8: Labelling-Scheme-Based Subgraph Query Processing on Graph Data.
9: Advanced Querying and Mining Aspects of Graph Databases.
10: G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases.
11: TEDI: Efficient Shortest Path Query Answering on Graphs.
12: Graph Mining Techniques: Focusing on Discriminating between Real and Synthetic Graphs.
13: Matrix Decomposition-Based Dimensionality Reduction on Graph Data.
14: Clustering Vertices in Weighted Graphs.
15: Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks.
16: Graph Representation Anonymization in Large Survey Rating Data.
17: Graph Database Applications in Various Domains.
18: Querying RDF Data.
19: On the Efficiency of Querying and Storing RDF Documents.
20: Graph Applications in Chemoinformatics and Structural Bioinformatics.
21: Business Process Graphs: Similarity Search and Matching.
22: A Graph-Based Approach for Semantic Process Model Discovery.
23: Shortest Path in Transportation Network and Weighted Subdivisions.
About the Contributors.