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Explaining Results of Path Queries on Graphs

Single-Path Results for Context-Free Path Queries

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Software Foundations for Data Interoperability and Large Scale Graph Data Analytics (SFDI 2020, LSGDA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1281))

Abstract

Many graph query languages use, at their core, path queries that yield node pairs that are connected by a path of interest. For the end-user, such node pairs only give limited insight as to why this query result is obtained, as the pair does not directly identify the underlying path of interest. To address this limitation of path queries, we propose the single-path semantics, which evaluates path queries to, for each node pair (m, n), a single path from m to n satisfying the conditions of the query. To put our proposal in practice, we provide an efficient algorithm for evaluating context-free path queries, a particular powerful type of path queries, using the single-path semantics. Additionally, we perform a short evaluation of our techniques that shows that the single-path semantics is practically feasible, even when query results grow large.

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Authors and Affiliations

  1. Department of Computer Science, University of California, Davis, Davis, CA, 95616-8562, USA

    Jelle Hellings

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  1. Jelle Hellings
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Correspondence to Jelle Hellings .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Lu Qin

  2. The University of New South Wales, Sydney, NSW, Australia

    Wenjie Zhang

  3. University of Technology Sydney, Sydney, NSW, Australia

    Ying Zhang

  4. The University of New South Wales, Sydney, NSW, Australia

    You Peng

  5. National Institute of Informatics, Tokyo, Japan

    Hiroyuki Kato

  6. The University of New South Wales, Sydney, NSW, Australia

    Wei Wang

  7. Osaka University, Osaka, Japan

    Chuan Xiao

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Hellings, J. (2020). Explaining Results of Path Queries on Graphs. In: Qin, L., et al. Software Foundations for Data Interoperability and Large Scale Graph Data Analytics. SFDI LSGDA 2020 2020. Communications in Computer and Information Science, vol 1281. Springer, Cham. https://doi.org/10.1007/978-3-030-61133-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-61133-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61132-3

  • Online ISBN: 978-3-030-61133-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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