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Encyclopedia of Big Data Technologies pp 890–898Cite as

Graph Query Processing

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While being eminently useful in a wide variety of application domains, the high expressiveness of graph queries makes them hard to optimize and, hence, challenging to process efficiently. We discuss a number of state-of-the-art approaches which aim to overcome these challenges, focusing specifically on planning, optimization, and execution of two commonly used types of declarative graph queries: subgraph queries and regular path queries.

Overview

In this chapter, we give a broad overview of the general techniques for processing two classes of graph queries: subgraph queries, which find instances of a query subgraph in a larger input graph, and regular path queries (RPQs), which find pairs of nodes in the graph connected by a path matching a given regular expression. Certainly, software that manage and process graph-structured data, such as RDF triple stores (Neumann and Weikum (2010), Virtuoso), graph database management systems (GDBMSes) (Kankanamge et al. (2017), neo4j),...

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Notes

  1. 1.

    The only exception we are aware of is the LogicBlox system (Aref et al. 2015)

  2. 2.

    This can be achieved, for example, by first checking the sizes of the adjacency lists that need to be intersected and starting intersecting the elements in the smallest list in others.

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Author information

Authors and Affiliations

  1. University of Waterloo, Waterloo, ON, Canada

    S. Salihoglu

  2. Eindhoven University of Technology, Eindhoven, Netherlands

    N. Yakovets

Authors
  1. S. Salihoglu
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  2. N. Yakovets
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Corresponding authors

Correspondence to S. Salihoglu or N. Yakovets .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. School of Information Technologies, Sydney University, Sydney, Australia

    Albert Y. Zomaya

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Salihoglu, S., Yakovets, N. (2019). Graph Query Processing. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_215

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