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DataDiff: User-Interpretable Data Transformation Summaries for Collaborative Data Analysis

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Published:27 May 2018Publication History

ABSTRACT

Interest in collaborative dataset versioning has emerged due to complex, ad-hoc, and collaborative nature of data science, and the need to record and reason about data at various stages of pre-processing, cleaning, and analysis. To support effective collaborative dataset versioning, one critical operation is differentiation : to succinctly describe what has changed from one dataset to the next. Differentiation, or diffing, allows users to understand changes between two versions, to better understand the evolution process, or to support effective merging or conflict detection across versions. We demonstrate DataDiff, a practical and concise data-diff tool that provides human-interpretable explanations of changes between datasets without reliance on the operations that led to the changes.

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      cover image ACM Conferences
      SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data
      May 2018
      1874 pages
      ISBN:9781450347037
      DOI:10.1145/3183713

      Copyright © 2018 ACM

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      Publication History

      • Published: 27 May 2018

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      SIGMOD '18 Paper Acceptance Rate90of461submissions,20%Overall Acceptance Rate785of4,003submissions,20%

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