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Author ORCID Identifier
Open Access Dissertation
Doctor of Philosophy (PhD)
Year Degree Awarded
Month Degree Awarded
Peter J. Haas
Databases and Information Systems | Operations Research, Systems Engineering and Industrial Engineering
Constrained optimization problems are at the heart of significant applications in a broad range of domains, including finance, transportation, manufacturing, and healthcare. They are often found at the final step of business analytics, namely prescriptive analytics, to allow businesses to transform a rich understanding of data, typically provided by advanced predictive models, into actionable decisions. Modeling and solving these problems has relied on application-specific solutions, which are often complex, error-prone, and do not generalize. Our goal is to create a domain-independent, declarative approach, supported and powered by the system where the data relevant to these problems typically resides: the database. Despite their widespread importance, declarative and scalable solutions to support prescriptive analytics close to the data did not exist prior to this thesis.
This thesis presents a complete system that supports package queries, a new query model that extends traditional database queries to handle complex constraints and preferences over answer sets, allowing the declarative specification and efficient evaluation of a significant class of constrained optimization problems–integer programs–within a database. Package queries pose unique challenges to a database system, ranging from their richer expressive power, more complex semantics, and harder computational complexity than their SQL counterpart, to scalability issues that arise from large amounts of data and uncertainty in the data. This thesis presents a unified system to address all these challenges. It further demonstrates the performance, quality, and applicability of our solutions with real-world problems from finance, healthcare, and science.
Brucato, Matteo, "Enabling Declarative and Scalable Prescriptive Analytics in Relational Data" (2021). Doctoral Dissertations. 2278.
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Available for download on Thursday, September 01, 2022