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Efficient detection of emergency event from moving object data streams
conference contribution
posted on 2014-01-01, 00:00 authored by L Guo, Guangyan HuangGuangyan Huang, Z DingThe 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.
History
Event
19th International Conference on Database Systems for Advanced Applications (19th : 2014 : Bali, Indonesia)Volume
8422Issue
Part IISeries
Lecture Notes in Computer SciencePagination
422 - 437Publisher
SpringerLocation
Bali, IndonesiaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2014-04-21End date
2014-04-24ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319058139Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2014, SpringerEditor/Contributor(s)
S Bhowmick, C Dyreson, C Jensen, M Lee, A Muliantara, B ThalheimTitle of proceedings
19th International Conference, DASFAA 2014, Bali, Indonesia, April 21-24, 2014. Proceedings, Part IIUsage metrics
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