by Dan Suciu (Author), Dan Olteanu (Author), Christopher Re (Author) Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-k query processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. Table of Contents: Overview / Data and Query Model / The Query Evaluation Problem / Extensional Query Evaluation / Intensional Query Evaluation / Advanced Techniques Author Biography Dan Suciu is a Professor in Computer Science at the University of Washington. He received his Ph.D. from the University of Pennsylvania in 1995, then was a principal member of the technical staff at AT&T Labs until he joined the University of Washington in 2000. Professor Suciu is conducting research in data management, with an emphasis on topics that arise from sharing data on the Internet, such as management of semistructured and heterogeneous data, data security, and managing data with uncertainties. He is a co-author of the book Data on the Web: from Relations to Semistructured Data and XML. He holds twelve US patents, received the 2000 ACM SIGMOD Best Paper Award, the 2010 PODS Ten Years Best paper award, and is a recipient of the NSF Career Award and of an Alfred P.