Cleaning uncertain streams by parallelized probabilistic graphical models

Authors:
Qian Zhang;Shan Wang;Biao Qin
Affiliations:
DEKE Lab, Renmin University of China, Beijing, China;DEKE Lab, Renmin University of China, Beijing, China;DEKE Lab, Renmin University of China, Beijing, China
Venue:
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Year:
2010

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Abstract

Real-world applications generate uncertain streams due to unreliable equipments and/or data processing such as object identification. However, application context implies specific rules, which are critical in cleaning data and make them closer to the reality. In this paper, we propose a framework for cleaning uncertain streams by Parallelized Probabilistic Graphical Models (P2GM). Making full use of multi-core processing architecture, the system processes parallelized high-volume streams efficiently. With P2GM, users can define their own cleaning algorithms and generate specific parallelized systems. We implement a prototype of video surveillance based on P2GM, and demonstrate the quality and performance of our approaches experimentally.