During the past few years, the number of applications that need to process large-scale data has grown remarkably. The data driving these applications are often uncertain, as is the analysis, which often involves probabilistic models and statistical inference. Examples include sensor-based monitoring, information extraction, and online advertising. Such applications require probabilistic data analysis (PDA), which is a family of queries over data, uncertainties, and probabilistic models that involve relational operators from database literature, as well as inference operators from statistical machine learning (SML) literature. Prior to our work, probabilistic database research advocated an approach in which uncertainty is modeled by attaching probabilities to data items. However, such systems do not and cannot take advantage of the wealth of SML research, because they are unable to represent and reason the pervasive probabilistic correlations in the data.

In this thesis, we propose, build, and evaluate BayesStore, a probabilistic database system that natively supports SML models and various inference algorithms to perform advanced data analysis. This marriage of database and SML technologies creates a declarative and efficient probabilistic processing framework for applications dealing with large-scale uncertain data. We use sensor-based monitoring and information extraction over text as the two driving applications. Sensor network applications generate noisy sensor readings, on top of which a first-order Bayesian network model is used to capture the probability distribution. Information extraction applications generate uncertain entities from text using linear-chain conditional random fields. We explore a variety of research challenges, including extending the relational data model with probabilistic data and statistical models, efficiently implementing statistical inference algorithms in a database, defining relational operators (e.g., select, project, join) over probabilistic data and models, developing joint optimization of inference operators and the relational algebra, and devising novel query execution plans. The experimental results show: (1) statistical inference algorithms over probabilistic models can be efficiently implemented in the set-oriented programming framework in databases; (2) optimizations for query-driven SML inference lead to orders-of-magnitude speed-up on large corpora; and (3) using in-database SML methods to extract and query probabilistic information can significantly improve answer quality.