Incorporating Uncertainty in Data Management and Integration

Abstract

Modern-day applications like information extraction on the web, data integration, entity resolution, scientific data management, and sensor data management are all required to cope with uncertainty in data. Motivated by this observation, recent years have witnessed a surge of research in the field of uncertain databases. The basic goal of this research is to abstract the common challenges and develop principled, general, and efficient techniques for dealing with uncertainty in the context of data management systems. This thesis makes advances in the field of uncertain data management by presenting efficient techniques for managing and integrating uncertain data. Specifically, the contributions may be classified under three areas: (1) Generalizing: We generalize uncertain databases to incorporate continuous probability distributions and incomplete information; (2) Integration: We establish foundations for integration of uncertain data sources; (3) Efficiency: We develop efficient algorithms for joins and indexing over uncertain data.