TY - GEN
T1 - Camel
T2 - 19th International Conference on Database Systems for Advanced Applications, DASFAA 2014
AU - Yu, Yaxin
AU - Huang, Xudong
AU - Zhu, Xinhua
AU - Wang, Guoren
PY - 2014
Y1 - 2014
N2 - A Journey Group T-Pattern (JG T-Pattern) is a special kind of T-Pattern(Trajectory Pattern) in which a large number of users walked through a common trajectory; also, it allows users depart from the trajectory for several times. Travel route is an instance of Journey Group and hot travel route can then be mined under the help of Camel. Instagram is a popular photo-sharing smart phone application based on social network, it is widely used among tourists to record their journey. In this paper, we focus on data generated by Instagram to discover the JG T-pattern of travel routes. Previous researches on T-pattern mining focus on GPS-based data, which is different from the UGC-based(User Generated Content based) data. Data of the former is dense because it is often generated automatically in a certain pace, while the latter is sparse because it is UGC-based, which means the data is generated by the uploading of users. Therefore, a novel approach, called Journey Group T-pattern Mining strategy, is proposed to deal with the trajectory mining on sparse location data. The demo shows that Camel is an efficient and effective system to discover Journey Groups.
AB - A Journey Group T-Pattern (JG T-Pattern) is a special kind of T-Pattern(Trajectory Pattern) in which a large number of users walked through a common trajectory; also, it allows users depart from the trajectory for several times. Travel route is an instance of Journey Group and hot travel route can then be mined under the help of Camel. Instagram is a popular photo-sharing smart phone application based on social network, it is widely used among tourists to record their journey. In this paper, we focus on data generated by Instagram to discover the JG T-pattern of travel routes. Previous researches on T-pattern mining focus on GPS-based data, which is different from the UGC-based(User Generated Content based) data. Data of the former is dense because it is often generated automatically in a certain pace, while the latter is sparse because it is UGC-based, which means the data is generated by the uploading of users. Therefore, a novel approach, called Journey Group T-pattern Mining strategy, is proposed to deal with the trajectory mining on sparse location data. The demo shows that Camel is an efficient and effective system to discover Journey Groups.
UR - http://www.scopus.com/inward/record.url?scp=84958537164&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-05813-9_37
DO - 10.1007/978-3-319-05813-9_37
M3 - Conference contribution
AN - SCOPUS:84958537164
SN - 9783319058122
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 527
EP - 530
BT - Database Systems for Advanced Applications - 19th International Conference, DASFAA 2014, Proceedings
PB - Springer Verlag
Y2 - 21 April 2014 through 24 April 2014
ER -