Querying large graph databases

  • Authors:
  • Yiping Ke;James Cheng;Jeffrey Xu Yu

  • Affiliations:
  • Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong;Division of Information Systems School of Computer Engineering, Nanyang Technological University, Singapore;Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

  • Venue:
  • DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
  • Year:
  • 2010

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Abstract

Graph exists ubiquitously in a wide spectrum of application domains, such as protein structures in biology, chemical compounds in chemistry, food webs in ecology, social networks, Web graphs, P2P networks, and many more. With the increasing popularity of graph databases, how to assess graph data effectively and efficiently becomes an important research problem. Considerable research efforts have been devoted to developing advanced query processing techniques on graph databases. This tutorial presents a comprehensive survey on methodologies and techniques for querying large graph databases, including subgraph and supergraph query processing, structural similarity query processing, correlation search in transaction graph databases, connection query processing and approximate matching in large graphs. The tutorial is prepared for database and data mining researchers who are interested in complex data types that can be generally modeled as graphs.