Stochastic Package Queries in Probabilistic Databases

Matteo Brucato, Nishant Yadav, Azza Abouzied, Peter J. Haas, Alexandra Meliou

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We provide methods for in-database support of decision making under uncertainty. Many important decision problems correspond to selecting a "package" (bag of tuples in a relational database) that jointly satisfy a set of constraints while minimizing some overall "cost" function; in most real-world problems, the data is uncertain. We provide methods for specifying - -via a SQL extension - -and processing stochastic package queries (SPQS), in order to solve optimization problems over uncertain data, right where the data resides. Prior work in stochastic programming uses Monte Carlo methods where the original stochastic optimization problem is approximated by a large deterministic optimization problem that incorporates many "scenarios", i.e., sample realizations of the uncertain data values. For large database tables, however, a huge number of scenarios is required, leading to poor performance and, often, failure of the solver software. We therefore provide a novel ßs algorithm that, instead of trying to solve a large deterministic problem, seamlessly approximates it via a sequence of smaller problems defined over carefully crafted "summaries" of the scenarios that accelerate convergence to a feasible and near-optimal solution. Experimental results on our prototype system show that ßs can be orders of magnitude faster than prior methods at finding feasible and high-quality packages.

Original languageEnglish (US)
Title of host publicationSIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Number of pages15
ISBN (Electronic)9781450367356
StatePublished - Jun 14 2020
Event2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States
Duration: Jun 14 2020Jun 19 2020

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Conference2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
Country/TerritoryUnited States


  • data integration
  • decision making
  • optimization
  • package queries
  • portfolio optimization
  • prescriptive analytics
  • probabilistic databases
  • simulation
  • stochastic programming

ASJC Scopus subject areas

  • Software
  • Information Systems


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