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Encyclopedia of Big Data Technologies pp 1–6Cite as

Graph Exploration and Search

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Definition

Exploratory methods have been proposed as a mean to extract knowledge from relational data without knowing what to search (Idreos et al. 2015). Graph exploration has been introduced to perform exploratory analyses on graph-shaped data (Mottin and Müller 2017). Graph exploration aims at mitigating the access to the data to the user, even if such user is a novice.

Algorithms for graph exploration assume the user is not able to completely specify the object of interest with a structured query like a SPARQL (see chapter “Graph Query Languages”), but rather expresses the need with a simpler, more ambiguous language.

This asymmetry between the rigidity of structured queries and ambiguity of the user has inspired the study of approximate, flexible, and example-based methods.

Overview

The research on graph exploration has revolved around three main pillars: keyword graph queries, exploratory graph analysis, and refinement of query results.

Keyword graph queries

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Author information

Authors and Affiliations

  1. Hasso Plattner Institute, Potsdam, Germany

    Davide Mottin

  2. Washington State University, Pullman, WA, USA

    Yinghui Wu

Authors
  1. Davide Mottin
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  2. Yinghui Wu
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Corresponding authors

Correspondence to Davide Mottin or Yinghui Wu .

Editor information

Editors and Affiliations

  1. School of Comp. Sci. and Engineering, University of New South Wales School of Comp. Sci. and Engineering, Eveleigh, New South Wales, Australia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. Dresden Database Systems Group, Technische Universität Dresden, 01062, Dresden, Germany

    Hannes Voigt

  2. Department of Mathematics and Computer Science, Eindhoven University of Technology http://www.win.tue.nl/~gfletche/

    George Fletcher

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Mottin, D., Wu, Y. (2018). Graph Exploration and Search. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_80-1

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  • DOI: https://doi.org/10.1007/978-3-319-63962-8_80-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

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