DeQL: A Decision Query Language for Prescriptive Analytics over Relational Data

[2606.19751] DeQL: A Decision Query Language for Prescriptive Analytics over Relational Data Skip to main content Search Submit Donate Log in Computer Science > Databases arXiv:2606.19751 (cs) [Submitted on 18 Jun 2026 ( v1 ), last revised 26 Aug 2026 (this version, v2)] Title: DeQL: A Decision Query Language for Prescriptive Analytics over Relational Data Authors: Matteo Brucato , Fjodor Kholodkov , Soren Little , Jakob Mayer , Duc Nguyen View PDF HTML (experimental) Abstract: DeQL (Decision Query Language) extends SQL to express decision queries: given options drawn from relational data, constraints from policy, and a measurable objective, a DeQL query computes the best course of action. Two constructs carry the core of the extension: CREATE CANDIDATES, which defines the space of options from relational sources, and DECIDE, which declares decision variables, named constraints, and an objective over them. The design follows SQL's principles: the user states what to optimize while the engine chooses how to solve it, every query consumes and produces relations, and the structure of a problem stays visible to the engine. This document specifies the language: its design principles, syntax, formal grammar, and execution model. The examples span subset selection, allocation, assignment, scheduling, and decisions at multiple levels of aggregation. The specification also covers extensions for optimization under uncertainty, inline model scoring, and time- and quality-bounded solving. It is an early version of the specification; the language is under active development, and this version fixes the core constructs on which later revisions will build. Comments: 33 pages, 5 tables, no figures. Version 0.2. Corrects several rules stated incorrectly in version 0.1 (SELECTION desugaring scope, auto-multiply over MIN, MAX and AVG, minimax objective directions, the PaQL REPEAT translation, and the grain of scheduling constraints) and closes several gaps in the formal grammar Subjects: Databases (cs.DB) ; Optimization and Control (math.OC) Cite as: arXiv:2606.19751 [cs.DB] (or arXiv:2606.19751v2 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2606.19751 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Matteo Brucato [ view email ] [v1] Thu, 18 Jun 2026 03:31:28 UTC (29 KB) [v2] Wed, 26 Aug 2026 20:21:27 UTC (36 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.DB < prev | next > new | recent | 2026-06 Change to browse by: cs math math.OC References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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