A “semi-lazy” approach to probabilistic path prediction in dynamic environments
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatiotemporal periodical pattern mining in traffic data
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational) 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 large amount of data and analyzing them with spatial, temporal and other multi-dimensional information. Many traditional approaches are not feasible for such analysis since they use numeric measures and cannot describe the complex atypical events. In this study, we propose a new model of atypical cluster to effectively represent those events and efficiently retrieve them from massive data. The micro-cluster is designed to summarize individual events, and the macro-cluster is used to integrate the information from multiple event. 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.