| Title: | Efficient detection of emergency event from moving object data streams |
| Author: | Guo, Limin (1)
; Huang, Guangyan (2)
; Ding, Zhiming (1)
|
| Conference Name: | 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014
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| Conference Date: | April 21, 2014 - April 24, 2014
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| Issued Date: | 2014
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| Conference Place: | Bali, Indonesia
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| Publish Place: | Springer Verlag
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| Indexed Type: | CPCI
; EI
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| ISSN: | 3029743
|
| ISBN: | 978-3-319-05813-9; 978-3-319-05812-2
|
| Department: | (1) Institute of Software, Chinese Academy of Sciences, China; (2) Centre for Applied Informatics, School of Engineering and Science, Victoria University, Australia
|
| Abstract: | 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. |
| English Abstract: | 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. |
| Language: | 英语
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| Content Type: | 会议论文
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| URI: | http://ir.iscas.ac.cn/handle/311060/16513
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| Appears in Collections: | 软件所图书馆_会议论文
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| Recommended Citation: | |
Guo, Limin ,Huang, Guangyan ,Ding, Zhiming . Efficient detection of emergency event from moving object data streams[C]. 见:19th International Conference on Database Systems for Advanced Applications, DASFAA 2014. Bali, Indonesia. April 21, 2014 - April 24, 2014.
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