2010 Fall Colloquium: Amol Deshpande

Fall Colloquium presents Professor Amol Deshpande.

  • Title: Managing and Querying Large Probabilistic Databases
  • Date: Monday October 4, 2010
  • Time: 4pm
  • Location: CSIC Building, Room 1115

Increasing numbers of real-world application domains are generating data that is inherently noisy, incomplete, and probabilistic in nature. Statistical analysis and probabilistic inference, widely used in those domains, often introduce additional layers of uncertainty. Examples include sensor data analysis, data integration and information extraction on the Web, social network analysis, and scientific and biomedical data management. Managing and querying such data requires us to combine the tools and the techniques from a variety of disciplines including databases, first-order logic, and probabilistic reasoning. There has been much work at the intersection of these research areas in recent years. The work on probabilistic databases has made great advances in efficiently executing SQL and inference queries over large-scale uncertain datasets. The research in first-order probabilistic models and lifted inference has resulted in several techniques for efficiently integrating first-order logic and probabilistic reasoning.

In this talk, I will present some of the foundations of large-scale probabilistic data management, and the challenges in scaling the representational power and the reasoning capabilities of probabilistic databases. I will primarily focus on the PrDB probabilistic data management system that we are building here at the University of Maryland. Unlike the other work on this topic, PrDB is designed to represent uncertain data with rich correlation structures, and it uses probabilistic graphical models as the basic representation model. I will discuss how PrDB supports compact specification of uncertainties at different abstraction levels, from "schema-level" uncertainties that apply to entire relations to "tuple-specific" uncertainties that apply to a specific tuple or a specific set of tuples

Interest Areas: 

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