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The relative ease of collaborative data science and analysis has led to aproliferation of many thousands or millions of $versions$ of the same datasetsin many scientific and commercial domains, acquired or constructed at variousstages of data analysis across many users, and often over long periods of time.Managing, storing, and recreating these dataset versions is a non-trivial task.The fundamental challenge here is the $storage-recreation\;trade-off$: the morestorage we use, the faster it is to recreate or retrieve versions, while theless storage we use, the slower it is to recreate or retrieve versions. Despitethe fundamental nature of this problem, there has been a surprisingly littleamount of work on it. In this paper, we study this trade-off in a principledmanner: we formulate six problems under various settings, trading off thesequantities in various ways, demonstrate that most of the problems areintractable, and propose a suite of inexpensive heuristics drawing fromtechniques in delay-constrained scheduling, and spanning tree literature, tosolve these problems. We have built a prototype version management system, thataims to serve as a foundation to our DATAHUB system for facilitatingcollaborative data science. We demonstrate, via extensive experiments, that ourproposed heuristics provide efficient solutions in practical dataset versioningscenarios.

Principles of Dataset Versioning: Exploring the Recreation/Storage Tradeoff