You are not logged in.

Efficient detection of emergency event from moving object data streams

Guo, Limin, Huang, Guangyan and Ding, Zhiming 2014, Efficient detection of emergency event from moving object data streams, in 19th International Conference, DASFAA 2014, Bali, Indonesia, April 21-24, 2014. Proceedings, Part II, Springer, Berlin, Germany, pp. 422-437, doi: 10.1007/978-3-319-05813-9_28.

Attached Files
Name Description MIMEType Size Downloads

Title Efficient detection of emergency event from moving object data streams
Author(s) Guo, Limin
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Ding, Zhiming
Conference name 19th International Conference on Database Systems for Advanced Applications (19th : 2014 : Bali, Indonesia)
Conference location Bali, Indonesia
Conference dates 21-24 Apr. 2014
Title of proceedings 19th International Conference, DASFAA 2014, Bali, Indonesia, April 21-24, 2014. Proceedings, Part II
Editor(s) Bhowmick,SS
Dyreson,CE
Jensen,CS
Lee,ML
Muliantara,A
Thalheim,B
Publication date 2014
Series Lecture Notes in Computer Science v.8422
Start page 422
End page 437
Total pages 16
Publisher Springer
Place of publication Berlin, Germany
Summary 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.
ISBN 9783319058139
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-05813-9_28
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083699

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 5 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 82 Abstract Views, 4 File Downloads  -  Detailed Statistics
Created: Fri, 27 May 2016, 15:49:23 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.