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Explaining Dataset Changes for Semantic Data Versioning with Explain-Da-V (Technical Report)
Databases · Submitted 30 Jan 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We empirically show, using an adapted existing benchmark and a newly created benchmark, that \texttt{Explain-Da-V} generates better explanations than existing data transformation synthesis methods.
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The abstract, sentence by sentence
In multi-user environments in which data science and analysis is collaborative, multiple versions of the same datasets are generated. While managing and storing data versions has received some attention in the research literature, the semantic nature of such changes has remained under-explored. In this work, we introduce \texttt{Explain-Da-V}, a framework aiming to explain changes between two given dataset versions. \texttt{Explain-Da-V} generates \emph{explanations} that use \emph{data transformations} to explain changes. We further introduce a set of measures that evaluate the validity, generalizability, and explainability of these explanations. We empirically show, using an adapted existing benchmark and a newly created benchmark, that \texttt{Explain-Da-V} generates better explanations than existing data transformation synthesis methods.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In multi-user environments in which data science and analysis is collaborative, multiple versions of the same datasets are generated. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | While managing and storing data versions has received some attention in the research literature, the semantic nature of such changes has remained under-explored. | gap 0.99 | 0.05 | 0.02 | asserted 0.98 |
| s2 | In this work, we introduce \texttt{Explain-Da-V}, a framework aiming to explain changes between two given dataset versions. | contribution 1.00 | 0.88 | 0.10 | asserted 0.99 |
| s3 | \texttt{Explain-Da-V} generates \emph{explanations} that use \emph{data transformations} to explain changes. | method 0.79 | 0.83 | 0.07 | asserted 1.00 |
| s4 | We further introduce a set of measures that evaluate the validity, generalizability, and explainability of these explanations. | contribution 0.87 | 0.95 | 0.07 | asserted 1.00 |
| s5 | We empirically show, using an adapted existing benchmark and a newly created benchmark, that \texttt{Explain-Da-V} generates better explanations than existing data transformation synthesis methods. | result 1.00 | 0.97 | 0.82 | asserted 1.00 |
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The paper
Original abstract
In multi-user environments in which data science and analysis is collaborative, multiple versions of the same datasets are generated. While managing and storing data versions has received some attention in the research literature, the semantic nature of such changes has remained under-explored. In this work, we introduce \texttt{Explain-Da-V}, a framework aiming to explain changes between two given dataset versions. \texttt{Explain-Da-V} generates \emph{explanations} that use \emph{data transformations} to explain changes. We further introduce a set of measures that evaluate the validity, generalizability, and explainability of these explanations. We empirically show, using an adapted existing benchmark and a newly created benchmark, that \texttt{Explain-Da-V} generates better explanations than existing data transformation synthesis methods.
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What this paper does with the arXiv papers it cites
What it cites · 1 sentence without a confident label
Cites as support 1 paper
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fewer chunks of information is favorable [ref] .
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation · Explanation Explainability · 0.98
Cites as background 1 paper
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Multiple methods find or discover related tables [ref] (e.g., joinable [ref] and unionable [ref] ).
SANTOS: Relationship-based Semantic Table Union Search · Data Versioning · 0.92
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