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Books > Computing & IT > Computer communications & networking
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Probabilistic Databases (Paperback)
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Probabilistic Databases (Paperback)
Series: Synthesis Lectures on Data Management
Expected to ship within 10 - 15 working days
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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
General
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