Browsing by Author "Yu, Xiao"

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Multidimensional Analysis of Atypical Events in Cyber-Physical Data
(2012)
Tang, Lu-An
;
Yu, Xiao
;
Kim, Sangkyum
;
Han, Jiawei
;
Peng, Wen-Chih
A Cyber-Physical System (CPS) integrates physical devices (e. g., sensors, cameras) with cyber components to form a situation-integrated analytical system that may respond intelligently to dynamic changes of the real-world situations. CPS claims many promising applications, such as traffic observation, battlefield surveillance and sensor-network-based monitoring. One important research topic in CPS is about the atypical event analysis, i.e., retrieving the events from a large amount of data and analyzing them with spatial, temporal and other multidimensional information. Many traditional approaches are not feasible for such analysis since they cannot describe the complex atypical events. In this study, we propose a new model of the atypical cluster to effectively represent such events and efficiently retrieve them from massive data. The micro-cluster is designed to summarize an individual event, and the macro-cluster is used to integrate the information from multiple events. To facilitate scalable, flexible and online analysis, the concept of significant cluster is defined and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real datasets with the size of more than 50 GB. The results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.
Multidimensional Sensor Data Analysis in Cyber-Physical System: An Atypical Cube Approach
(2012)
Tang, Lu-An
;
Yu, Xiao
;
Kim, Sangkyum
;
Han, Jiawei
;
Peng, Wen-Chih
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 50 GB; the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.