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

Computer Science > Databases [Submitted on 18 Jun 2026] Title:DeQL: A Decision Query Language for Prescriptive Analytics over Relational Data 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 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) with examples spanning subset selection, allocation, assignment, scheduling, and decisions at multiple levels of aggregation, and extensions for optimization under uncertainty, inline model scoring, and time- and quality-bounded solving. It is the first version of the specification; the language is under active development, and this version fixes the core constructs on which later revisions will build. Current browse context: cs.DB References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) 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.