Abstract

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.

Highlights

  • The Cyber-Physical System (CPS) has been a focused research theme recently due to its wide applications in the areas of traffic monitoring, battlefield surveillance, and sensornetwork-based monitoring [1,2,3,4,5,6]

  • Since the main theme of this study is on multidimensional analysis of atypical event, we assume that the atypical criteria are given and clean atypical records can be retrieved by CPS

  • We focus on atypical duration in this paper, the proposed approach is flexible to adjust to other domain-specific measures

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Summary

Introduction

The Cyber-Physical System (CPS) has been a focused research theme recently due to its wide applications in the areas of traffic monitoring, battlefield surveillance, and sensornetwork-based monitoring [1,2,3,4,5,6]. This paper substantially extends the ICDE 2012 conference version [8], in the following ways: (1) introducing the concepts of atypical cube as an integrated model for multidimensional sensor data analysis in CPS; (2) proposing the techniques to process OLAP queries based on the atypical cube, including the algorithms for both the large-scale and small-scale (i.e., drill-through) queries; (3) discussing the issues of extending atypical cube to other dimensions and introducing a case study in traffic application; (4) carrying out the time complexity analysis of proposed algorithms; (5) providing complete formal proofs for all the properties and propositions; (6) covering related work in more details and including recent ones; (7) introducing the bottom-up styled cube in more details as the background knowledge; (8) expanding the performance studies on real datasets.

Backgrounds and Preliminaries
Atypical Cube Construction
OLAP Query Processing
Performance Evaluation
Extensions and Discussions
Related Works
Findings
Conclusions and Future Work
Full Text
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