Efficient detection of emergency event from moving object data streams

ISCAS OpenIR

	Efficient detection of emergency event from moving object data streams
	Guo, Limin (1); Huang, Guangyan (2); Ding, Zhiming (1)
	2014
会议名称	19th International Conference on Database Systems for Advanced Applications, DASFAA 2014
页码	422-437
会议日期	April 21, 2014 - April 24, 2014
会议地点	Bali, Indonesia
收录类别	CPCI ; EI
出版地	Springer Verlag
ISSN	3029743
ISBN	978-3-319-05813-9; 978-3-319-05812-2
部门归属	(1) Institute of Software, Chinese Academy of Sciences, China; (2) Centre for Applied Informatics, School of Engineering and Science, Victoria University, Australia
摘要	The advance of positioning technology enables us to online collect moving object data streams for many applications. One of the most significant applications is to detect emergency event through observed abnormal behavior of objects for disaster prediction. However, the continuously generated moving object data streams are often accumulated to a massive dataset in a few seconds and thus challenge existing data analysis techniques. In this paper, we model a process of emergency event forming as a process of rolling a snowball, that is, we compare a size-rapidly-changed (e.g., increased or decreased) group of moving objects to a snowball. Thus, the problem of emergency event detection can be resolved by snowball discovery. Then, we provide two algorithms to find snowballs: a clustering-and-scanning algorithm with the time complexity of O(n ²) and an efficient adjacency-list-based algorithm with the time complexity of O(nlogn). The second method adopts adjacency lists to optimize efficiency. Experiments on both real-world dataset and large synthetic datasets demonstrate the effectiveness, precision and efficiency of our algorithms © 2014 Springer International Publishing Switzerland.; The advance of positioning technology enables us to online collect moving object data streams for many applications. One of the most significant applications is to detect emergency event through observed abnormal behavior of objects for disaster prediction. However, the continuously generated moving object data streams are often accumulated to a massive dataset in a few seconds and thus challenge existing data analysis techniques. In this paper, we model a process of emergency event forming as a process of rolling a snowball, that is, we compare a size-rapidly-changed (e.g., increased or decreased) group of moving objects to a snowball. Thus, the problem of emergency event detection can be resolved by snowball discovery. Then, we provide two algorithms to find snowballs: a clustering-and-scanning algorithm with the time complexity of O(n ²) and an efficient adjacency-list-based algorithm with the time complexity of O(nlogn). The second method adopts adjacency lists to optimize efficiency. Experiments on both real-world dataset and large synthetic datasets demonstrate the effectiveness, precision and efficiency of our algorithms © 2014 Springer International Publishing Switzerland.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16513
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Guo, Limin ,Huang, Guangyan ,Ding, Zhiming . Efficient detection of emergency event from moving object data streams[C]. Springer Verlag,2014:422-437.