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Cancun, Mexico
Apr. 7, 2008 to Apr. 12, 2008
ISBN: 978-1-4244-1836-7
pp: 1160-1169
Bhargav Kanagal , University of Maryland. bhargav@cs.umd.edu
Amol Deshpande , University of Maryland. amol@cs.umd.edu
ABSTRACT
In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases.
CITATION
Bhargav Kanagal, Amol Deshpande, "Online Filtering, Smoothing and Probabilistic Modeling of Streaming data", ICDE, 2008, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2008, pp. 1160-1169, doi:10.1109/ICDE.2008.4497525
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