TY - GEN
T1 - ROAM
T2 - 7th SIAM International Conference on Data Mining
AU - Li, Xiaolei
AU - Han, Jiawei
AU - Kim, Sangkyum
AU - Gonzalez, Hector
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. One important application with such data is automated identification of suspicious movements. Due to the sheer volume of data associated with moving objects, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spa-tiotemporal features.
AB - With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. One important application with such data is automated identification of suspicious movements. Due to the sheer volume of data associated with moving objects, it is challenging to develop a method that can efficiently and effectively detect anomalies. The problem is exacerbated by the fact that anomalies may occur at arbitrary levels of abstraction and be associated with multiple granularity of spa-tiotemporal features.
UR - http://www.scopus.com/inward/record.url?scp=70449100637&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449100637&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:70449100637
SN - 9780898716306
T3 - Proceedings of the 7th SIAM International Conference on Data Mining
SP - 273
EP - 284
BT - Proceedings of the 7th SIAM International Conference on Data Mining
Y2 - 26 April 2007 through 28 April 2007
ER -