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UNMASQUE: A Hidden SQL Query Extractor

Khurana, K and Haritsa, JR (2020) UNMASQUE: A Hidden SQL Query Extractor. In: Proceedings of the VLDB Endowment, 13 (12). pp. 2809-2812.

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Official URL: https://doi.org/10.14778/3415478.3415481


Given a database instance and a populated result, query reverse-engineering attempts to identify candidate SQL queries that produce this result on the instance. A variant of this problem arises when a ground-truth is additionally available, but hidden within an opaque database application. In this demo, we present UN-MASQUE, an extraction algorithm that is capable of precisely identifying a substantive class of such hidden queries. A hallmark of its design is that the extraction is completely non-invasive to the application. Specifically, it only examines the results obtained from application executions on databases derived with a combination of data mutation and data generation techniques, thereby achieving platform-independence. Further, potent optimizations, such as database size reduction to a few rows, are incorporated to minimize the extraction overheads. The demo showcases these features on both declarative and imperative applications. © VLDB Endowment. All rights reserved.

Item Type: Journal Article
Publication: Proceedings of the VLDB Endowment
Publisher: VLDB Endowment
Additional Information: The copyright for this article belongs to VLDB Endowment.
Keywords: Query languages; Query processing; Reverse engineering, Application execution; Data generation; Data mutation; Database applications; Extraction algorithms; Generation techniques; Ground truth; Optimisations; Platform independence; SQL query, Extraction
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 07 Feb 2023 10:41
Last Modified: 07 Feb 2023 10:41
URI: https://eprints.iisc.ac.in/id/eprint/79973

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