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Supporting Better Insights of Data Science Pipelines with Fine-grained Provenance

Lookup NU author(s): Professor Paolo Missier

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Abstract

© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. Successful data-driven science requires complex data engineering pipelines to clean, transform, and alter data in preparation for machine learning, and robust results can only be achieved when each step in the pipeline can be justified, and its effect on the data explained. In this framework, we aim at providing data scientists with facilities to gain an in-depth understanding of how each step in the pipeline affects the data, from the raw input to training sets ready to be used for learning. Starting from an extensible set of data preparation operators commonly used within a data science setting, in this work we present a provenance management infrastructure for generating, storing, and querying very granular accounts of data transformations, at the level of individual elements within datasets whenever possible. Then, from the formal definition of a core set of data science preprocessing operators, we derive a provenance semantics embodied by a collection of templates expressed in PROV, a standard model for data provenance. Using those templates as a reference, our provenance generation algorithm generalises to any operator with observable input/output pairs. We provide a prototype implementation of an application-level provenance capture library to produce, in a semiautomatic way, complete provenance documents that account for the entire pipeline. We report on the ability of that reference implementation to capture provenance in real ML benchmark pipelines and over TCP-DI synthetic data. We finally show how the collected provenance can be used to answer a suite of provenance benchmark queries that underpin some common pipeline inspection questions, as expressed on the Data Science Stack Exchange.

Publication metadata

Author(s): Chapman A, Lauro L, Missier P, Torlone R

Publication type: Article

Publication status: Published

Journal: ACM Transactions on Database Systems

Year: 2024

Volume: 49

Issue: 2

Print publication date: 10/04/2024

Online publication date: 09/02/2024

Acceptance date: 24/01/2024

ISSN (print): 0362-5915

ISSN (electronic): 1557-4644

Publisher: ACM

URL: https://doi.org/10.1145/3644385

DOI: 10.1145/3644385

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