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BAYESSTORE: Managing large, uncertain data repositories with probabilistic graphical models
(EN)
Γαροφαλακης Μινως
(EL)
Michelakis Eirinaios
(EN)
Hellerstein, Joseph, 1952-
(EN)
Garofalakis Minos
(EN)
Wang Daisy Zhe
(EN)
Πολυτεχνείο Κρήτης
(EL)
Technical University of Crete
(EN)
Several real-world applications need to effectively manage and reason about
large amounts of data that are inherently uncertain. For instance, pervasive
computing applications must constantly reason about volumes of noisy
sensory readings for a variety of reasons, including motion prediction and
human behavior modeling. Such probabilistic data analyses require sophisticated
machine-learning tools that can effectively model the complex
spatio/temporal correlation patterns present in uncertain sensory data. Unfortunately,
to date, most existing approaches to probabilistic database systems
have relied on somewhat simplistic models of uncertainty that can be
easily mapped onto existing relational architectures: Probabilistic information
is typically associated with individual data tuples, with only limited
or no support for effectively capturing and reasoning about complex data
correlations. In this paper, we introduce BAYESSTORE, a novel probabilistic
data management architecture built on the principle of handling statistical
models and probabilistic inference tools as first-class citizens of the
database system. Adopting a machine-learning view, BAYESSTORE employs
concise statistical relational models to effectively encode the correlation
patterns between uncertain data, and promotes probabilistic inference
and statistical model manipulation as part of the standard DBMS operator
repertoire to support efficient and sound query processing. We present
BAYESSTORE’s uncertainty model based on a novel, first-order statistical
model, and we redefine traditional query processing operators, to manipulate
the data and the probabilistic models of the database in an efficient
manner. Finally, we validate our approach, by demonstrating the value of
exploiting data correlations during query processing, and by evaluating a
(EN)
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