Extracting Social Web from Moving Object Trajectories

Abstract:

The advent of a lot of mobile social networking applications on the smart phones has greatly changed the way how people interact with each other. The core function requiring by these applications is the location-based service providing by the sensors inside the phones. Accumulating rich location sequences, the phone actually can tell more than merely the location information. In this paper, we try to discover the beneath social web from these location information. In other words, we propose a novel trajectory mining approach to dig the social web from collections of such location information. Such approach permits to perform relationship mining from trajectory-generated content. Given a temporal threshold and a spatial threshold, our approach carries out trajectory based mining to identify who are in closer relationship with the querying moving object. The comprehensive evaluation of the approach has demonstrated very promising results and is also analyzed in this paper.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 2468-2471
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.2468
• Online since: August 2013
• Authors: Jian Dai
• Keywords: Moving Object, Social Web, Trajectory

References

[1] D. Pfoser, and C. S. Jensen, Capturing the Uncertainty of Moving-Object Representations. SSD, (1999).

[2] L. Chen, M. Tamer,V. Oria, Robust and Fast Similarity Search for Moving Object Trajectories, SIGMOD, (2005).

DOI: 10.1145/1066157.1066213

[3] E. Frentzos, K. Gratsias, Y. Theodoridis. Index-based Most Similar Trajectory Search, In Proc. of ICDE, (2007).

DOI: 10.1109/icde.2007.367927

[4] F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, Trajectory Pattern Mining, In Proc. of SIGKDD, (2007).

DOI: 10.1145/1281192.1281230

[5] J. -G. Lee, J. Han, and K. -Y. Whang, Trajectory clustering: a partition-and-group framework. SIGMOD, (2007).

[6] N. Pelekis, I. Kopanakis, I. Ntoutsi, G. Marketos, G. Andrienko and Y. Theodoridis. Similarity Search in Trajectory Databases. In Proc. of TIME, (2007).

DOI: 10.1109/time.2007.59

[7] O. Abul, F. Bonchi, M. Nanni, Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases, In Proc. of ICDE, (2008).

DOI: 10.1109/icde.2008.4497446

[8] T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: An Efficient Data Clustering Method for Very Large Databases, In Proc. of SIGMOD, (1996).

DOI: 10.1145/235968.233324