SimClone: Detecting Tabular Data Clones Using Value Similarity

Xu Yang

doi:10.1145/3676961

SimClone: Detecting Tabular Data Clones Using Value Similarity

2024-12-27

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AI

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Xu Yang

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Gopi Krishnan Rajbahadur

DOI：10.1145/3676961

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Abstract

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Data clones are defined as multiple copies of the same data among datasets. The presence of data clones between datasets can cause issues such as difficulties in managing data assets and data license violations when using datasets with clones to build AI software. However, detecting data clones is not trivial. The majority of the prior studies in this area rely on structural information to detect data clones (e.g., font size, column header). However, tabular datasets used to build AI software are typically stored without any structural information. In this article, we propose a novel method called SimClone for data clone detection in tabular datasets without relying on structural information. SimClone method utilizes value similarities for data clone detection. We also propose a visualization approach as a part of our SimClone method to help locate the exact position of the cloned data between a dataset pair. Our results show that our SimClone outperforms the current state-of-the-art method by at least 20% in terms of both F1-score and AUC. In addition, SimClone's visualization component helps identify the exact location of the data clone in a dataset with a Precision@10 value of 0.80 in the top 20 true positive predictions.

Keywords:

Data clone

Tabular clone

Value Similarity

Machine learning datasets

SimClone: Detecting Tabular Data Clones Using Value Similarity

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