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.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Business Process Graphs
Clustering Vertices in Weighted Graphs
Graph Applications in Chemoinformatics
Graph Indexing Querying Techniques
Kernel-Based Similarity Searches
Large Scale Graph Mining
Querying RDF
Real and Synthetic Graphs
Relational Approaches for Graph Pattern Matching
Semantic Process Model Discovery

Reviews and Testimonials

This book is the first that approaches the challenges associated with graphs from a data management point of view; it connects the dots. As I am currently involved in building a native graph database engine, I encounter problems that arise from every possible aspect: data representation, indexing, transaction support, parallel query processing, and may others. All of them sound familiar to a database researcher, but the inherent change is fundamental as they originate from a new foundation. I found that this book contains a lot of timely information, aiding my efforts. To be clear, it does not offer the blueprint for building a graph database system, but it contains a bag of diamonds, enlightening the readers as they start exploring a field that may fundamentally change data management in the future.

– Haixun Wang, Microsoft Research Asia

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Editor/Author Biographies

Sherif Sakr, Ph.D., is a Research Scientist in the Managing Complexity Group at National ICT Australia (NICTA), ATP lab, Sydney, Australia. He is also a Conjoint Lecturer in The School of Computer Science and Engineering (CSE) at University of New South Wales (UNSW) and an Adjunct Lecturer with the Department of Computing in the Division of Information and Communication Sciences at Macquarie University . He received his PhD degree in Computer Science from Konstanz University, Germany in 2007. He received his BSc and MSc degree in Computer Science from the Information Systems department at the Faculty of Computers and Information in Cairo University, Egypt, in 2000 and 2003 respectively. His research interest is data and information management in general, particularly in areas of indexing techniques, query processing and optimization techniques, graph data management, social networks, data management in cloud computing.

Eric Pardede, Ph.D., is a lecturer in the Department of Computer Science and Computer Engineering at La Trobe University, Melbourne, Australia. From the same university, he received his Doctor of Philosophy and Master of Information Technology in 2006 and 2002 respectively. He has research interests in data modelling, data quality, data security and data privacy in XML and Web Databases as well as data repository for social networks.

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Editorial Review Board

Sourav S. Bhowmick, Nanyang Technological University, Singapore
Michael Böhlen, University of Zurich, Switzerland
Marlon Dumas, University of Tartu, Estonia
Claudio Gutierrez, Universidad de Chile, Chile
Jun Huan, University of Kansas, USA
Irwin King, The Chinese University of Hong Kong, China
Raymond Wong, University of New South Wales, Australia
Mohammed Zaki, Rensselaer Polytechnic Institute, USA
Xiaofang Zhou, University of Queensland, Australia