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DiscoPG: Property Graph Schema Discovery and Exploration

Abstract : Property graphs are becoming pervasive in a variety of graph processing applications using interconnected data. They allow to encode multi-labeled nodes and edges, as well as their properties, represented as key/value pairs. Although property graphs are widely used in several open-source and commercial graph databases, they lack a schema definition, unlike their relational counterparts. The property graph schema discovery problem consists of extracting the underlying schema concepts and types from such graph datasets. We showcase DiscoPG, a system for efficiently and accurately discovering and exploring property graph schemas. To this end, it leverages hierarchical clustering using a Gaussian Mixture Model, which accounts for both node labels and properties. DiscoPG allows users to perform schema discovery for both static and dynamic graph datasets. Suitable visualization layouts and dedicated dashboards enable the user perception of the static and dynamic inferred schema on the node clusters, as well as the differences in runtimes and clustering quality. To the best of our knowledge, DiscoPG is the first system to tackle the property graph schema discovery problem. As such, it supports the insightful exploration of the graph schema components and their evolving behavior, while revealing the underpinnings of the clustering-based discovery process.
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Contributor : Stefania Dumbrava Connect in order to contact the contributor
Submitted on : Wednesday, September 21, 2022 - 2:59:05 PM
Last modification on : Friday, September 30, 2022 - 11:34:15 AM


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Angela Bonifati, Stefania Dumbrava, Emile Martinez, Fatemeh Ghasemi, Malo Jaffré, et al.. DiscoPG: Property Graph Schema Discovery and Exploration. 48th International Conference on Very Large Data Bases, Sep 2022, Sydney, Australia. pp.3654-3657, ⟨10.14778/3554821.3554867⟩. ⟨hal-03771388⟩



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