DiscoPG

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.

Finding the PG schema of any (semi)structured dataset: a tale of graphs and abstraction

“…Also, edges may have their own attributes, something that is not possible in relational model. The popularity of PGs has led to efforts to standardize the data model, the query language, and to generate a schema from a given graph [7], [16]. A recent, comprehensive proposal for PG schemas (in the classical database sense: schema defined independently of a dataset, introducing types that the dataset may or may not validate) is [2].…”