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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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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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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.

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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

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].

References

  1. Azza Abouzied, Peter J. Haas, and Alexandra Meliou. In-database decision support: Opportunities and challenges. IEEE Data Engineering Bulletin, 45(3):102-115, September 2022. URL: http://sites.computer.org/debull/A22sept/p102.pdf.
  2. Gordon J Alexander and Alexandre M Baptista. A comparison of var and cvar constraints on portfolio selection with the mean-variance model. Management science, 50(9):1261-1273, 2004. URL: https://doi.org/10.1287/MNSC.1040.0201.
  3. Dimitris Bertsimas and John Tsitsiklis. Introduction to Linear Optimization. Athena Scientific, 1st edition, 1997. Google Scholar
  4. Matteo Brucato, Azza Abouzied, and Alexandra Meliou. A scalable execution engine for package queries. SIGMOD Record, 46(1):24-31, 2017. URL: https://doi.org/10.1145/3093754.3093761.
  5. Matteo Brucato, Azza Abouzied, and Alexandra Meliou. Package queries: efficient and scalable computation of high-order constraints. The VLDB Journal, 27(1), 2018. URL: https://doi.org/10.1007/s00778-017-0483-4.
  6. Matteo Brucato, Juan Felipe Beltran, Azza Abouzied, and Alexandra Meliou. Scalable package queries in relational database systems. PVLDB, 9(7):576-587, 2016. URL: https://doi.org/10.14778/2904483.2904489.
  7. Matteo Brucato, Nishant Yadav, Azza Abouzied, Peter J. Haas, and Alexandra Meliou. Stochastic package queries in probabilistic databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), pages 269-283, 2020. URL: https://doi.org/10.1145/3318464.3389765.
  8. Marco C Campi, Simone Garatti, and Maria Prandini. The scenario approach for systems and control design. Annual Reviews in Control, 33(2):149-157, 2009. URL: https://doi.org/10.1016/J.ARCONTROL.2009.07.001.
  9. Wang Cen and Peter J. Haas. Enhanced simulation metamodeling via graph and generative neural networks. In Winter Simulation Conference, WSC 2022, Singapore, December 11-14, 2022, pages 2748-2759. IEEE, 2022. URL: https://doi.org/10.1109/WSC57314.2022.10015361.
  10. Davide Frazzetto, Thomas Dyhre Nielsen, Torben Pedersen, and Laurynas Siksnys. Prescriptive analytics: a survey of emerging trends and technologies. The VLDB Journal, May 2019. DOI: 10.1007/s00778-019-00539-y. URL: https://doi.org/10.1007/s00778-019-00539-y.
  11. Peter J. Haas, Paul P. Maglio, Patricia G. Selinger, and Wang Chiew Tan. Data is dead... without what-if models. PVLDB, 4(12):1486-1489, 2011. URL: http://www.vldb.org/pvldb/vol4/p1486-haas.pdf.
  12. Riddho R. Haque, Anh L. Mai, Matteo Brucato, Azza Abouzied, Peter J. Haas, and Alexandra Meliou. Stochastic sketchrefine: Scaling in-database decision-making under uncertainty to millions of tuples. CoRR, abs/2411.17915, 2024. URL: https://doi.org/10.48550/arXiv.2411.17915.
  13. Ravi Jampani, Fei Xu, Mingxi Wu, Luis Leopoldo Perez, Chris Jermaine, and Peter J. Haas. The Monte Carlo Database System: Stochastic analysis close to the data. ACM Trans. Database Syst., 36(3):18:1-18:41, 2011. URL: https://doi.org/10.1145/2000824.2000828.
  14. Sujin Kim, Raghu Pasupathy, and Shane G Henderson. A guide to sample average approximation. In Handbook of Simulation Optimization, pages 207-243. Springer, 2015. Google Scholar
  15. Irv Lustig, Brenda Dietrich, Christer Johnson, and Christopher Dziekan. The analytics journey. Analytics Magazine, november/december 2010. URL: http://analytics-magazine.org/the-analytics-journey.
  16. Anh L. Mai, Pengyu Wang, Azza Abouzied, Matteo Brucato, Peter J. Haas, and Alexandra Meliou. Scaling package queries to a billion tuples via hierarchical partitioning and customized optimization. PVLDB, 17, 2024. URL: https://doi.org/10.14778/3641204.3641222.
  17. Alexander J. McNeil, Rüdiger Frey, and Paul Embrechts. Quantitative Risk Management: Concepts, Techniques and Tools. Princeton University Press, second edition, 2015. Google Scholar
  18. Sergey Sarykalin, Gaia Serraino, and Stan Uryasev. Value-at-risk vs. conditional value-at-risk in risk management and optimization. In State-of-the-Art Decision-Making Tools in the Information-Intensive Age, pages 270-294. INFORMS TutORials in Operations Research, September 2008. URL: https://doi.org/10.1287/educ.1080.0052.
  19. Laurynas Siksnys and Torben Bach Pedersen. SolveDB: Integrating optimization problem solvers into SQL databases. In SSDBM, pages 14:1-14:12, 2016. URL: https://doi.org/10.1145/2949689.2949693.
  20. Laurynas Siksnys, Torben Bach Pedersen, Thomas Dyhre Nielsen, and Davide Frazzetto. SolveDB+: Sql-based prescriptive analytics. In Proc. EDBT, pages 133-144, 2021. URL: https://doi.org/10.5441/002/EDBT.2021.13.
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