TY - CHAP
T1 - Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data
AU - Li, Zhenhui
AU - Han, Jiawei
N1 - Funding Information:
The work was supported in part by Boeing company, NASA NRA-NNH10ZDA001N, NSF IIS-0905215 and IIS-1017362, the U.S. Army Research Laboratory under Cooperative Agreement No. W911NF-09-2-0053 (NS-CTA) and startup funding provided by the Pennsylvania State University. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agencies.
Publisher Copyright:
© 2014, Springer-Verlag Berlin Heidelberg.
PY - 2014
Y1 - 2014
N2 - As spatiotemporal data becomes widely available, mining and understanding such data have gained a lot of attention recently. Among all important patterns, periodicity is arguably the most frequently happening one for moving objects. Finding periodic behaviors is essential to understanding the activities of objects, and to predict future movements and detect anomalies in trajectories. However, periodic behaviors in spatiotemporal data could be complicated, involvingmultiple interleaving periods, partial time span, and spatiotemporal noises and outliers. Even worse, due to the limitations of positioning technology or its various kinds of deployments, real movement data is often highly incomplete and sparse. In this chapter, we discuss existing techniques to mine periodic behaviors from spatiotemporal data, with a focus on tackling the aforementioned difficulties risen in real applications. In particular, we first review the traditional time-series method for periodicity detection. Then, a novelmethod specifically designed to mine periodic behaviors in spatiotemporal data, Periodica, is introduced. Periodica proposes to use reference spots to observe movement and detect periodicity from the in-and-out binary sequence. Then, we discuss the important issue of dealing with sparse and incomplete observations in spatiotemporal data, and propose a new general framework Periodo to detect periodicity for temporal events despite such nuisances.We provide experiment results on real movement data to verify the effectiveness of the proposed methods. While these techniques are developed in the context of spatiotemporal data mining, we believe that they are very general and could benefit researchers and practitioners from other related fields.
AB - As spatiotemporal data becomes widely available, mining and understanding such data have gained a lot of attention recently. Among all important patterns, periodicity is arguably the most frequently happening one for moving objects. Finding periodic behaviors is essential to understanding the activities of objects, and to predict future movements and detect anomalies in trajectories. However, periodic behaviors in spatiotemporal data could be complicated, involvingmultiple interleaving periods, partial time span, and spatiotemporal noises and outliers. Even worse, due to the limitations of positioning technology or its various kinds of deployments, real movement data is often highly incomplete and sparse. In this chapter, we discuss existing techniques to mine periodic behaviors from spatiotemporal data, with a focus on tackling the aforementioned difficulties risen in real applications. In particular, we first review the traditional time-series method for periodicity detection. Then, a novelmethod specifically designed to mine periodic behaviors in spatiotemporal data, Periodica, is introduced. Periodica proposes to use reference spots to observe movement and detect periodicity from the in-and-out binary sequence. Then, we discuss the important issue of dealing with sparse and incomplete observations in spatiotemporal data, and propose a new general framework Periodo to detect periodicity for temporal events despite such nuisances.We provide experiment results on real movement data to verify the effectiveness of the proposed methods. While these techniques are developed in the context of spatiotemporal data mining, we believe that they are very general and could benefit researchers and practitioners from other related fields.
KW - Binary Sequence
KW - Periodic Behavior
KW - Periodic Pattern
KW - Potential Period
KW - Spatiotemporal Data
UR - http://www.scopus.com/inward/record.url?scp=85132905757&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85132905757&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40837-3_2
DO - 10.1007/978-3-642-40837-3_2
M3 - Chapter
AN - SCOPUS:85132905757
T3 - Studies in Big Data
SP - 41
EP - 81
BT - Studies in Big Data
PB - Springer
ER -