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QUARRY: A Graph Model for Queryable Association Rules

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AI 2022: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13728))

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Abstract

Association rule mining is a pivotal technique for knowledge discovery, but often involves time-intensive manual labour when performed on large datasets. In this paper we propose a solution for this problem: QUARRY, a graph model that enables consumable and queryable insights from association rules. In contrast to existing systems which take a list of rules and display them in a purpose-built visualisation, our graph-based model enables association rules to be queried directly via graph queries. Through a case study on maintenance data we show how this model enhances knowledge discovery by eliminating the need for domain experts to trawl through large lists of rules to find useful information. QUARRY, which is designed for compatibility with existing knowledge graphs, provides users with the means to easily search for rules pertaining to specific items as well as roll up and drill down on their searches using the concept hierarchy. Domain experts may also query for association rules based on transaction properties such as costs and dates, enabling critical insights into their data.

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Notes

  1. 1.

    The dataset and source code of QUARRY is available on GitHub.

  2. 2.

    https://neo4j.com.

  3. 3.

    https://neo4j.com/docs/cypher-manual/current/.

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Acknowledgments

This research is supported by the Australian Research Council through the Centre for Transforming Maintenance through Data Science (grant number IC180100030), funded by the Australian Government.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia

    Michael Stewart

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  1. Michael Stewart
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Correspondence to Michael Stewart .

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Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Haris Aziz

  2. University of Western Australia, Perth, WA, Australia

    Débora Corrêa

  3. University of Western Australia, Perth, WA, Australia

    Tim French

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Stewart, M. (2022). QUARRY: A Graph Model for Queryable Association Rules. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_22

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  • DOI: https://doi.org/10.1007/978-3-031-22695-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22694-6

  • Online ISBN: 978-3-031-22695-3

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