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BAYESSTORE: Managing large, uncertain data repositories with probabilistic graphical models

Wang Daisy Zhe, Michelakis Eirinaios, Garofalakis Minos, Hellerstein, Joseph, 1952-

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/DBEB1C6C-3ECB-4CE3-8C94-9A4AE8921D59-
Αναγνωριστικόhttp://db.cs.berkeley.edu/papers/vldb08-bayesstore.pdf-
Αναγνωριστικόhttps://doi.org/10.14778/1453856.1453896-
Γλώσσαen-
Μέγεθος12 pagesen
ΤίτλοςBAYESSTORE: Managing large, uncertain data repositories with probabilistic graphical modelsen
ΔημιουργόςWang Daisy Zheen
ΔημιουργόςMichelakis Eirinaiosen
ΔημιουργόςGarofalakis Minosen
ΔημιουργόςΓαροφαλακης Μινωςel
ΔημιουργόςHellerstein, Joseph, 1952-en
ΕκδότηςAssociation for Computing Machineryen
ΠερίληψηSeveral 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 aen
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-11-30-
Ημερομηνία Δημοσίευσης2008-
Θεματική ΚατηγορίαDatabase managementen
Θεματική ΚατηγορίαData repositoriesen
Βιβλιογραφική ΑναφοράD.Z. Wang, E. Michelakis, M. Garofalakis and J.M. Hellerstein, "BAYESSTORE: managing large, uncertain data repositories with probabilistic graphical models", in 34th International Conference on Very Large Data Bases, 2008.en

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