@article{AbouziedHM2022,
    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. In the context of our prior and ongoing work in
    this area, we discuss some strategies for addressing key challenges related
    to usability, scalability, data uncertainty, dynamic environments with
    changing data and models, and the need to support decision-making agents.
    We indicate how deep integration between the DBMS, predictive models, and
    optimization software creates opportunities for rich prescriptive-query
    functionality with good scalability and performance},

    Author = {Azza Abouzied and Peter J. Haas and Alexandra Meliou},
    Journal = {IEEE Data Engineering Bulletin},
    Number = {3},
    Pages = {102--115},
    Title = {\href{http://sites.computer.org/debull/A22sept/p102.pdf}{In-Database Decision Support: Opportunities and Challenges}},
    Type = {article},
    Venue = {DEBul},
    Volume = {45},
    Year = {2022},
    month = sep,
}