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...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Bhargav Kanagal, Amol Deshpande
• Keywords: Database, Dynamic Probabilistic Models, Efficient Query Processing, ICDE 2008, Probabilistic Database View