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Data Management Perspectives on Prescriptive Analytics (Invited Talk)

Authors Alexandra Meliou , Azza Abouzied , Peter J. Haas , Riddho R. Haque , Anh Mai , Vasileios Vittis

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Author Details

Alexandra Meliou
  • University of Massachusetts Amherst, MA, USA
Azza Abouzied
  • New York University, Abu Dhabi, UAE
Peter J. Haas
  • University of Massachusetts Amherst, MA, USA
Riddho R. Haque
  • University of Massachusetts Amherst, MA, USA
Anh Mai
  • New York University, Abu Dhabi, UAE
Vasileios Vittis
  • University of Massachusetts Amherst, MA, USA

Funding

  • Meliou, Alexandra: NSF IIS-2211918.
  • Abouzied, Azza: ASPIRE Award for Research Excellence (AARE-2020) grant AARE20-307; NYUAD CITIES, funded by Tamkeen under the Research Institute Award CG001.
  • Haas, Peter J.: NSF IIS-2211918.

Acknowledgements

This invited talk paper discusses prior and ongoing work, summarizing contributions from prior publications [Azza Abouzied et al., 2022; Anh L. Mai et al., 2024; Matteo Brucato et al., 2016; Matteo Brucato et al., 2020; Riddho R. Haque et al., 2024].

Cite As Get BibTex

Alexandra Meliou, Azza Abouzied, Peter J. Haas, Riddho R. Haque, Anh Mai, and Vasileios Vittis. Data Management Perspectives on Prescriptive Analytics (Invited Talk). In 28th International Conference on Database Theory (ICDT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 328, pp. 2:1-2:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICDT.2025.2

Abstract

Decision makers in a broad range of domains, such as finance, transportation, manufacturing, and healthcare, often need to derive optimal decisions given a set of constraints and objectives. Traditional solutions to such constrained optimization problems are typically application-specific, complex, and do not generalize. Further, the usual workflow requires slow, cumbersome, and error-prone data movement between a database, and predictive-modeling and optimization packages. All of these problems are exacerbated by the unprecedented size of modern data-intensive optimization problems. The emerging research area of in-database prescriptive analytics aims to provide seamless domain-independent, declarative, and scalable approaches powered by the system where the data typically resides: the database. Integrating optimization with database technology opens up prescriptive analytics to a much broader community, amplifying its benefits. We discuss how deep integration between the DBMS, predictive models, and optimization software creates opportunities for rich prescriptive-query functionality with good scalability and performance. Summarizing some of our main results and ongoing work in this area, we highlight challenges related to usability, scalability, data uncertainty, and dynamic environments, and argue that perspectives from data management research can drive novel strategies and solutions.

Subject Classification

ACM Subject Classification
  • Information systems → Decision support systems
  • Information systems → Database design and models
  • Information systems → Query languages
  • Information systems → Database query processing
  • Computing methodologies → Modeling and simulation
  • Applied computing → Decision analysis
Keywords
  • Prescriptive analytics
  • decision making
  • scalable constrained optimization

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References

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