Graph Data Management: Techniques and ApplicationsRelease Date: August, 2011. Copyright © 2012. 502 pages.
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DOI: 10.4018/978-1-61350-053-8, ISBN13: 9781613500538, ISBN10: 161350053X, EISBN13: 9781613500545 | | TopDescriptionGraphs 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. TopTable of Contents and List of Contributors|
1.
| Graph Representation
(pages 1-28)
D. Dominguez-Sal (Universitat Politècnica de Catalunya, Spain), V. Muntés-Mulero (Universitat Politècnica de Catalunya, Spain), N. Martínez-Bazán (Universitat Politècnica de Catalunya, Spain), J. Larriba-Pey (Universitat Politècnica de Catalunya, Spain)
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2.
| The Graph Traversal Pattern
(pages 29-46)
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3.
| Data, Storage and Index Models for Graph Databases
(pages 47-70)
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4.
| An Overview of Graph Indexing and Querying Techniques
(pages 71-88)
Sherif Sakr (University of New South Wales, Australia), Ghazi Al-Naymat (University of Tabuk, Saudi Arabia)
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5.
| Efficient Techniques for Graph Searching and Biological Network Mining
(pages 89-111)
Alfredo Ferro (Università di Catania, Italy), Rosalba Giugno (Università di Catania, Italy), Alfredo Pulvirenti (Università di Catania, Italy), Dennis Shasha (Courant Institute of Mathematical Sciences, USA)
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6.
| A Survey of Relational Approaches for Graph Pattern Matching over Large Graphs
(pages 112-141)
Jiefeng Cheng (The University of Hong Kong, China), Jeffrey Xu Yu (The Chinese University of Hong Kong, China)
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7.
| Labelling-Scheme-Based Subgraph Query Processing on Graph Data
(pages 142-174)
Hongzhi Wang (Harbin Institute of Technology, China), Jianzhong Li (Harbin Institute of Technology, China), Hong Gao (Harbin Institute of Technology, China)
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8.
| G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases
(pages 176-213)
Xiaohong Wang (University of Kansas, USA), Jun Huan (University of Kansas, USA), Aaron Smalter (University of Kansas, USA), Gerald H. Lushington (University of Kansas, USA)
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9.
| TEDI: Efficient Shortest Path Query Answering on Graphs
(pages 214-238)
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10.
| Graph Mining Techniques: Focusing on Discriminating between Real and Synthetic Graphs
(pages 239-259)
Ana Paula Appel (Federal University of Espirit, Brazil), Christos Faloutsos (Carnegie Mellon University, USA), Caetano Traina Junior (University of São Paulo at São Carlos, Brazil)
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11.
| Matrix Decomposition-Based Dimensionality Reduction on Graph Data
(pages 260-284)
Hiroto Saigo (Kyushu Institute of Technology, Japan), Koji Tsuda (National Institute of Advanced Industrial Science and Technology (AIST), Japan)
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12.
| Clustering Vertices in Weighted Graphs
(pages 285-298)
Derry Tanti Wijaya (Carnegie Mellon University, USA.), Stephane Bressan (National University of Singapore, Singapore)
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13.
| Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks
(pages 299-314)
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14.
| Graph Representation and Anonymization in Large Survey Rating Data
(pages 315-333)
Xiaoxun Sun (Australian Council for Educational Research, Australia), Min Li (University of Southern Queensland, Australia)
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15.
| Querying RDF Data
(pages 335-353)
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16.
| On the Efficiency of Querying and Storing RDF Documents
(pages 354-385)
Maria-Esther Vidal (Universidad Simón Bolívar, Venezuela), Amadís Martínez (Universidad Simón Bolívar &Universidad de Carabobo, Venezuela), Edna Ruckhaus (Universidad Simón Bolívar, Venezuela), Tomas Lampo (University of Maryland, USA), Javier Sierra (Universidad Simón Bolívar, Venezuela)
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17.
| Graph Applications in Chemoinformatics and Structural Bioinformatics
(pages 386-420)
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18.
| Business Process Graphs: Similarity Search and Matching
(pages 421-437)
Remco Dijkman (Eindhoven University of Technology, The Netherlands), Marlon Dumas (University of Tartu, Estonia), Luciano García-Bañuelos (University of Tartu, Estonia)
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19.
| A Graph-Based Approach for Semantic Process Model Discovery
(pages 438-462)
Ahmed Gater (Universite de Versailles Saint-Quentin en Yvelines, France), Daniela Grigori (Universite de Versailles Saint-Quentin en Yvelines, France), Mokrane Bouzeghoub (Universite de Versailles Saint-Quentin en Yvelines, France)
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20.
| Shortest Path in Transportation Network and Weighted Subdivisions
(pages 463-474)
Radwa Elshawi (National ICT Australia (NICTA), University of Sydney, Australia), Joachim Gudmundsson (National ICT Australia (NICTA), University of Sydney, Australia)
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TopTopics Covered- 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
TopPrefaceThe graph is a powerful tool for representing and understanding objects and their relationships in various application domains. Recently, graphs have been widely used to model many complex structured and schemaless data such as semantic web, social networks, biological networks, protein networks, chemical compounds and business process models. The growing popularity of graph databases has generated interesting data management problems. Therefore, the domain of graph databases have attracted a lot of attention from the research community and different challenges have been discussed such as: subgraph search queries, supergraph search queries, approximate subgraph matching, short path queries and graph mining techniques.
This book is designed for studying various fundamental challenges of storing and querying graph databases. In addition, it discusses the applications of graph databases in various domains. In particular, the book is divided into three main sections.
The first section discusses the basic definitions of graph data models, graph representations and graph traversal patterns. It also provides an overview of different graph indexing techniques and evaluation mechanisms for the main types of graph queries. The second section further discusses advanced querying aspects of graph databases and different mining techniques of graph databases. It should be noted that many graph querying algorithms are sensitive to the application scenario in which they are designed and cannot be generalized for all domains. Therefore, the third section focuses on presenting the usage of graph database techniques in different practical domains such as: semantic web, chemoinformatics, bioinformatics, business process model and transportation networks.
In a nutshell, the book provides a comprehensive summary from both of the algorithmic and the applied perspectives. It will provide the reader with a better understanding of how graph databases can be effectively utilized in different scenarios. TopAuthor(s)/Editor(s) BiographySherif Sakr 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 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. TopReviews 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 TopEditorial 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
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