PXML: A Probabilistic Semistructured Data Model and Algebra

Speaker: Edward HUNG

Abstract

Despite the recent proliferation of work on semistructured data models, there has been little work to date on supporting uncertainty in these models. In this talk, I will present a model for probabilistic semistructured data. The advantage of this approach is that it supports a flexible representation that allows the specification of a wide class of distributions over semistructured instances. I will provide two semantics for the model and show that the semantics are probabilistically coherent. Next, I will present an extension of the relational algebra to handle probabilistic semistructured data and describe efficient algorithms for answering queries that use this algebra. Finally, I will provide experimental results showing the efficiency of my algorithms.

Referred Papers