Cleaning Uncertain Streams by Parallelized Probabilistic Graphical Models

摘要：

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 (PZGM). 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 demon strate the quality and performance of our approaches experimentally.

关键词： Probabilistic graphic model data cleaning uncertain streams

作者: Qian Zhang Shan Wang Biao Qin

作者单位: DEKE Lab, Renmin University of China, Beijing 100872, China

会议类型: 国际会议

会议名称: 11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)

会议地点: 九寨沟

会议语种:英文

页码: 274-279

在线出版日期: 2010-07-14（万方平台首次上网日期，不代表论文的发表时间）