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Increasingly modern data science platforms today have non-intrusive andextensible provenance ingestion mechanisms to collect rich provenance andcontext information, handle modifications to the same file usingdistinguishable versions, and use graph data models (e.g., property graphs) andquery languages (e.g., Cypher) to represent and manipulate the storedprovenance/context information. Due to the schema-later nature of the metadata,multiple versions of the same files, and unfamiliar artifacts introduced byteam members, the "provenance graph" is verbose and evolving, and hard tounderstand; using standard graph query model, it is difficult to composequeries and utilize this valuable information.  In this paper, we propose two high-level graph query operators to address theverboseness and evolving nature of such provenance graphs. First, we introducea graph segmentation operator, which queries the retrospective provenancebetween a set of source vertices and a set of destination vertices via flexibleboundary criteria to help users get insight about the derivation relationshipsamong those vertices. We show the semantics of such a query in terms of acontext-free grammar, and develop efficient algorithms that run orders ofmagnitude faster than state-of-the-art. Second, we propose a graphsummarization operator that combines similar segments together to queryprospective provenance of the underlying project. The operator allows tuningthe summary by ignoring vertex details and characterizing local structures, andensures the provenance meaning using path constraints. We show the optimalsummary problem is PSPACE-complete and develop effective approximationalgorithms. The operators are implemented on top of a property graph backend.We evaluate our query methods extensively and show the effectiveness andefficiency of the proposed methods.

Understanding Data Science Lifecycle Provenance via Graph Segmentation and Summarization