TY - GEN
T1 - MoveMine
T2 - 2010 International Conference on Management of Data, SIGMOD '10
AU - Li, Zhenhui
AU - Ji, Ming
AU - Lee, Jae Gil
AU - Tang, Lu An
AU - Yu, Yintao
AU - Han, Jiawei
AU - Kays, Roland
PY - 2010
Y1 - 2010
N2 - With the maturity of GPS, wireless, and Web technologies, increasing amounts of movement data collected from various moving objects, such as animals, vehicles, mobile devices, and climate radars, have become widely available. Analyzing such data has broad applications, e.g., in ecological study, vehicle control, mobile communication management, and climatological forecast. However, few data mining tools are available for flexible and scalable analysis of massive-scale moving object data. Our system, MoveMine, is designed for sophisticated moving object data mining by integrating several attractive functions including moving object pattern mining and trajectory mining. We explore the state-of-the-art and novel techniques at implementation of the selected functions. A user-friendly interface is provided to facilitate interactive exploration of mining results and flexible tuning of the underlying methods. Since MoveMine is tested on multiple kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data. At the same time, it will benefit researchers to realize the importance and limitations of current techniques as well as the potential future studies in moving object data mining.
AB - With the maturity of GPS, wireless, and Web technologies, increasing amounts of movement data collected from various moving objects, such as animals, vehicles, mobile devices, and climate radars, have become widely available. Analyzing such data has broad applications, e.g., in ecological study, vehicle control, mobile communication management, and climatological forecast. However, few data mining tools are available for flexible and scalable analysis of massive-scale moving object data. Our system, MoveMine, is designed for sophisticated moving object data mining by integrating several attractive functions including moving object pattern mining and trajectory mining. We explore the state-of-the-art and novel techniques at implementation of the selected functions. A user-friendly interface is provided to facilitate interactive exploration of mining results and flexible tuning of the underlying methods. Since MoveMine is tested on multiple kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data. At the same time, it will benefit researchers to realize the importance and limitations of current techniques as well as the potential future studies in moving object data mining.
KW - moving objects
KW - pattern/trajectory mining
KW - visualization
UR - http://www.scopus.com/inward/record.url?scp=77954723630&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954723630&partnerID=8YFLogxK
U2 - 10.1145/1807167.1807319
DO - 10.1145/1807167.1807319
M3 - Conference contribution
AN - SCOPUS:77954723630
SN - 9781450300322
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 1203
EP - 1206
BT - Proceedings of the 2010 International Conference on Management of Data, SIGMOD '10
Y2 - 6 June 2010 through 11 June 2010
ER -