Joint Computer Science Colloquium/Everything Data Science Seminar

In-Database Decision Support: Opportunities and Challenges

Thursday, March 20, -
Speaker(s): Peter Haas

Snacks

Refreshments will be served at 3:45 PM.

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 give an overview of our work in this area, focusing on deterministic and stochastic "package queries", an optimization-oriented extension of traditional SQL queries. 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. 

Speaker Bio

Peter Haas joined the UMass faculty as a Professor of Information and Computer Sciences and Adjunct Professor of Mechanical and Industrial Engineering in the fall of 2017 after 30 years at IBM Research—where he rose to Principal Research Staff Member—and 20 years as a Consulting Professor in Management Science and Engineering at Stanford University. His research lies at the interface of information management, applied probability, statistics, machine learning, and computer simulation. He is a Fellow of both ACM and INFORMS and has received awards from IBM and both the Simulation and Computer Science communities, including an IBM Research Outstanding Innovation Award, an ACM SIGMOD 10-year Best Paper Award for his work on sampling-based exploration of massive datasets, and the INFORMS Simulation Society Outstanding Publication Award for his 2002 Springer monograph on Stochastic Petri Nets. Other work has included techniques for massive-scale data analytics (matrix completion, dynamic graph analysis, and declarative machine learning); Monte Carlo methods for scalable querying, stochastic optimization, and Bayesian learning over massive uncertain data; simulation metamodeling using neural networks and modular dynamic Bayes networks; automated relationship discovery in databases; query optimization methods; and autonomic computing. He serves on the editorial boards of ACM Transactions on Database Systems and ACM Transactions on Modeling and Computer Simulation and was an Associate Editor for the VLDB Journal from 2007-2013 and for Operations Research from 1995-2017. He is the author of roughly 160 conference publications, journal articles, and books, and has been granted over 30 patents, leading to his designation as an IBM Master Inventor. His work has been incorporated into IBM products and, more recently, the Apache DataSketches library.

Co-Sponsor(s)

Duke Computer Science; Duke Statistical Science; Duke Fuqua School of Business

Social Science 139

Contact

Jun Yang
junyang@cs.duke.edu