Uncertain Data Queries Processing in a Probabilistic Framework

Many applications today need to manage data that is uncertain, such as information extraction (IE), data integration, sensor RFID networks, and scientific experiments. Top-k queries are often natural and useful in analyzing uncertain data in those applications. In this paper, we study the problem of answering top-k queries in a probabilistic framework from a state-of-the-art statistical IE model-semi-Conditional Random Fields (CRFs)-in the setting of Probabilistic Databases that treat statistical models as first-class data objects. We investigate the problem of ranking the answers to Probabilistic Databases query. We present efficient algorithm for finding the best approximating parameters in such a framework to efficiently retrieve the top-k ranked results. An empirical study using real data sets demonstrates the effectiveness of probabilistic top-k queries and the efficiency of our method.