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arXiv · 2508.08054

TQL: Towards Type-Driven Data Discovery

Andrew Kang·Sainyam Galhotra
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Abstract

Existing query languages for data discovery exhibit system-driven designs that emphasize database features and functionality over user needs. We propose a re-prioritization of the client through an introduction of a language-driven approach to data discovery systems that can leverage powerful results from programming languages research. In this paper, we describe TQL, a flexible and practical query language which incorporates a type-like system to encompass downstream transformation-context in its discovery queries. The syntax and semantics of TQL (including the underlying evaluation model), are formally defined, and a sketch of its implementation is also provided. Additionally, we provide comparisons to existing languages for data retrieval and data discovery to examine the advantages of TQL's expanded expressive power in real-life settings.

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Andrew Kang, Sainyam Galhotra. 2025-08-11. TQL: Towards Type-Driven Data Discovery. https://doi.org/10.1109/bigdata62323.2024.10825227

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