Graph Data Management: Techniques and Applications

Part of the Advances in Data Mining and Database Management

Sherif Sakr (University of New South Wales, Australia) and Eric Pardede (LaTrobe University, Australia)

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DOI: 10.4018/978-1-61350-053-8, ISBN13: 9781613500538, ISBN10: 161350053X, EISBN13: 9781613500545

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MLA

Sakr, Sherif and Eric Pardede. "Graph Data Management: Techniques and Applications." IGI Global, 2012. 1-502. Web. 21 May. 2013. doi:10.4018/978-1-61350-053-8

APA

Sakr, S., & Pardede, E. (2012). Graph Data Management: Techniques and Applications (pp. 1-502). doi:10.4018/978-1-61350-053-8

Chicago

Sakr, Sherif and Eric Pardede. "Graph Data Management: Techniques and Applications." 1-502 (2012), accessed May 21, 2013. doi:10.4018/978-1-61350-053-8

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Description

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.

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Table of Contents and List of Contributors

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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)

In this chapter, we review different graph implementation alternatives that have been proposed in the literature. Our objective is to provide the readers with a broa... Sample PDF | More details...

$37.50

The Graph Traversal Pattern (pages 29-46)

Marko A. Rodriguez (AT&T Interactive, USA), Peter Neubauer (Neo Technology, Sweden)

A graph is a structure composed of a set of vertices (i.e. nodes, dots) connected to one another by a set of edges (i.e. links, lines). The concept of a graph has be... Sample PDF | More details...

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Data, Storage and Index Models for Graph Databases (pages 47-70)

Srinath Srinivasa (International Institute of Information Technology, India)

Management of graph structured data has important applications in several areas. Queries on such data sets are based on structural properties of the graphs, in addit... Sample PDF | More details...

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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)

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application... Sample PDF | More details...

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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)

From biochemical applications to social networks, graphs represent data. Comparing graphs or searching for motifs on such data often reveals interesting and useful p... Sample PDF | More details...

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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)

Due to rapid growth of the Internet and new scientific/technological advances, there exist many new applications that model data as graphs, because graphs have suffi... Sample PDF | More details...

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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)

When data are modeled as graphs, many research issues arise. In particular, there are many new challenges in query processing on graph data. This chapter studies the... Sample PDF | More details...

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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)

Our objective in this chapter is to enable fast similarity search in large graph databases with graph kernel functions. In particular, we propose (i) a novel kernel-... Sample PDF | More details...

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TEDI: Efficient Shortest Path Query Answering on Graphs (pages 214-238)

Fang Wei (University of Freiburg, Germany)

Our experimental results show that TEDI offers orders-of-magnitude performance improvement over existing approaches on the index construction time, the index size an... Sample PDF | More details...

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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)

Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A l... Sample PDF | More details...

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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)

Graph is a mathematical framework that allows us to represent and manage many real-world data such as relational data, multimedia data and biomedical data. When each... Sample PDF | More details...

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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)

Clustering is the unsupervised process of discovering natural clusters so that objects within the same cluster are similar and objects from different clusters are di... Sample PDF | More details...

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13.

Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks (pages 299-314)

Charalampos E. Tsourakakis (Carnegie Mellon University, USA)

In this Chapter, we present state of the art work on large scale graph mining using MapReduce. We survey research work on an important graph mining problem, counting... Sample PDF | More details...

$37.50

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)

We study the challenges of protecting privacy of individuals in the large public survey rating data in this chapter. Recent study shows that personal information in... Sample PDF | More details...

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15.

Querying RDF Data (pages 335-353)

Faisal Alkhateeb (Yarmouk University, Jordan), Jérôme Euzenat (INRIA & LIG, France)

This chapter provides an introduction to the RDF language as well as surveys the languages that can be used for querying RDF graphs. Then it reviews some of the lang... Sample PDF | More details...

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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)

In the context of the Semantic Web, different approaches have been defined to represent RDF documents, and the selected representation affects storage and time compl... Sample PDF | More details...

$37.50

17.

Graph Applications in Chemoinformatics and Structural Bioinformatics (pages 386-420)

Eleanor Joyce Gardiner (University of Sheffield, UK)

The focus of this chapter will be the uses of graph theory in chemoinformatics and in structural bioinformatics. There is a long history of chemical graph theory dat... Sample PDF | More details...

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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)

Organizations create collections of hundreds or even thousands of business process models to describe their operations. This chapter explains how graphs can be used... Sample PDF | More details...

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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)

One of the key tasks in the service oriented architecture that Semantic Web services aim to automate is the discovery of services that can fulfill the applications o... Sample PDF | More details...

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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)

In this chapter we consider two versions of the problem; the shortest path in a transportation network and the shortest path in a weighted subdivision, sometimes cal... Sample PDF | More details...

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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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Topics 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

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Preface

The 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.

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Author(s)/Editor(s) Biography

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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