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2008
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Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

9 years 10 months ago
Online Filtering, Smoothing and Probabilistic Modeling of Streaming data
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. he 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 o...
Bhargav Kanagal, Amol Deshpande
Added 01 Nov 2009
Updated 01 Nov 2009
Type Conference
Year 2008
Where ICDE
Authors Bhargav Kanagal, Amol Deshpande
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