Towards General-Purpose Data Discovery: A Programming Languages Approach

TL;DR

This paper introduces TQL, a domain-specific language for data discovery that leverages programming language research to enable expressive and formal query characterization, supported by a formal model and prototype.

Contribution

It presents TQL, a new formal language for data discovery, and its algebraic model ImpRAT, advancing the development of general-purpose data discovery tools.

Findings

Formal characterization of TQL using ImpRAT

Implementation of a modular prototype system

Enhanced expressiveness for data discovery queries

Abstract

Efficient and effective data discovery is critical for many modern applications in machine learning and data science. One major bottleneck to the development of a general-purpose data discovery tool is the absence of an expressive formal language, and corresponding implementation, for characterizing and solving generic discovery queries. To this end, we present TQL, a domain-specific language for data discovery well-designed to leverage and exploit the results of programming languages research in both its syntax and semantics. In this paper, we fully and formally characterize the core language through an algebraic model, Imperative Relational Algebra with Types (ImpRAT), and implement a modular proof-of-concept system prototype.