TY - JOUR
T1 - Multidimensional sensor data analysis in cyber-physical system
T2 - An atypical cube approach
AU - Tang, Lu An
AU - Yu, Xiao
AU - Kim, Sangkyum
AU - Han, Jiawei
AU - Peng, Wen Chih
AU - Sun, Yizhou
AU - Leung, Alice
AU - La Porta, Thomas
PY - 2012
Y1 - 2012
N2 - Cyber-Physical System (CPS) is an integration of distributed sensor networks with computational devices. CPS claims many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One important topic in CPS research is about the atypical event analysis, that is, retrieving the events from massive sensor data and analyzing them with spatial, temporal, and other multidimensional information. Many traditional methods are not feasible for such analysis since they cannot describe the complex atypical events. In this paper, we propose a novel model of atypical cluster to effectively represent such events and efficiently retrieve them from massive data. The basic cluster is designed to summarize an individual event, and the macrocluster is used to integrate the information from multiple events. To facilitate scalable, flexible, and online analysis, the atypical cube is constructed, and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real sensor datasets with the size of more than 50GB; the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.
AB - Cyber-Physical System (CPS) is an integration of distributed sensor networks with computational devices. CPS claims many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One important topic in CPS research is about the atypical event analysis, that is, retrieving the events from massive sensor data and analyzing them with spatial, temporal, and other multidimensional information. Many traditional methods are not feasible for such analysis since they cannot describe the complex atypical events. In this paper, we propose a novel model of atypical cluster to effectively represent such events and efficiently retrieve them from massive data. The basic cluster is designed to summarize an individual event, and the macrocluster is used to integrate the information from multiple events. To facilitate scalable, flexible, and online analysis, the atypical cube is constructed, and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real sensor datasets with the size of more than 50GB; the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.
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U2 - 10.1155/2012/724846
DO - 10.1155/2012/724846
M3 - Article
AN - SCOPUS:84862288427
SN - 1550-1329
VL - 2012
JO - International Journal of Distributed Sensor Networks
JF - International Journal of Distributed Sensor Networks
M1 - 724846
ER -