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Query Language and Access Methods for Graph Databases

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Managing and Mining Graph Data

Part of the book series: Advances in Database Systems ((ADBS,volume 40))

Abstract

With the prevalence of graph data in a variety of domains, there is an increasing need for a language to query and manipulate graphs with heterogeneous attributes and structures. We present a graph query language (GraphQL) that supports bulk operations on graphs with arbitrary structures and annotated at- tributes. In this language, graphs are the basic unit of information and each query manipulates one or more collections of graphs at a time. The core of GraphQL is a graph algebra extended from the relational algebra in which the selection operator is generalized to graph pattern matching and a composition operator is introduced for rewriting matched graphs. Then, we investigate access methods of the selection operator. Pattern matching over large graphs is challenging due to the NP-completeness of subgraph isomorphism. We address this by a combination of techniques: use of neighborhood subgraphs and pro- files, joint reduction of the search space, and optimization of the search order. Experimental results on real and synthetic large graphs demonstrate that graph specific optimizations outperform an SQL-based implementation by orders of magnitude.

This is a revised and extended version of the article “Graphs-at-a-time: Query Language and Access Methods for Graph Databases”, Huahai He and Ambuj K. Singh, In Proceedings of the 2008 ACMSIGMOD Conference, http://doi.acm.org/10.1145/1376616.1376660. Reprinted with permission of ACM.

Work done while at the University of California, Santa Barbara.

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Author information

Authors and Affiliations

  1. Google Inc., Mountain View, CA, 94043, USA

    Huahai He

  2. Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA

    Ambuj K. Singh

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  1. Huahai He
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  2. Ambuj K. Singh
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Correspondence to Huahai He .

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Editors and Affiliations

  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, U.S.A.

    Charu C. Aggarwal

  2. Microsoft Research Asia, Zhichun Road 49, Beijing, 100080, China, People's Republic

    Haixun Wang

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He, H., Singh, A.K. (2010). Query Language and Access Methods for Graph Databases. In: Aggarwal, C., Wang, H. (eds) Managing and Mining Graph Data. Advances in Database Systems, vol 40. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6045-0_4

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  • DOI: https://doi.org/10.1007/978-1-4419-6045-0_4

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