EAN: 9783031007514

SYNTHESIS LECTURES ON DATA MANAGEMENT PROBABI...

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

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