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大同大學資訊經營學系
雲端計算學程演講公告
演講題目: Mining trajectory patterns and routes from spatial-temporal
social media
演講者：交通大學 資訊工程系 彭文志 教授
演講分類：
雲端計算學程
邀請老師: 廖文華
日期:2013/02/25 時間：15:10—17:00
地點:北設工 11F Room1115
演講者簡介:
學歷：台大電機博士
經歷：
Wen-Chih Peng was born in Hsinchu, Taiwan, R.O.C in 1973. He received the BS and MS
degrees from the National Chiao Tung University, Taiwan, in 1995 and 1997, respectively,
and the Ph.D. degree in Electrical Engineering from the National Taiwan University,
Taiwan, R.O.C in 2001. Currently, he is an associate professor at the department of
computer science, National Chiao Tung University, Taiwan. Prior to joining the department
of Computer Science, National Chiao Tung University, he was mainly involved in the
projects related to mobile computing, data broadcasting and network data management. Dr.
Peng serves as PC members in several prestigious conferences, such as IEEE International
Conference on Data Engineering (ICDE), ACM International Conference on Knowledge
Discovery and Data Mining (ACM KDD), IEEE International Conference on Data Mining
(ICDM) and ACM International Conference on Information and Knowledge Management
(ACM CIKM). Dr. Peng is a co-organizer of 2nd International Workshop on
Privacy-Aware Location-based Mobile Services (PALMS) and is a guest editor of Signal
Processing (special issue on Information Processing and Data Management in Wireless
Sensor Networks). His research interests include mobile data management and data
mining. He is a member of IEEE.
大同大學資訊經營學系
演講摘要(或大綱):
The prevalence of smart phones leads to the advance of mobile Web and mobile social
applications. With location-acquisition technologies, a myriad of trajectories are produced
in different ways, such as check-in or photo sequences. In this talk, I will present key
techniques of mining trajectory patterns and routes from trajectories. Explicitly, we
propose a new trajectory pattern mining framework, namely, Clustering and Aggregating
Clues of Trajectories (CACT), for discovering trajectory routes that represent frequent
movement behaviors of a user. In addition to spatial and temporal biases, we observe that
trajectories contain silent durations, i.e., the time durations when no data points are
available to describe movements of users, which bring many challenge issues to trajectory
pattern mining. We claim that a movement behavior would leave some clues in its various
sampled/observed trajectories. These clues may be extracted from spatially and temporally
co-located data points from the observed trajectories. Based on this observation, we
propose clue-aware trajectory similarity to measure the clues between two trajectories.
Accordingly, we further propose the clue-aware trajectory clustering algorithm to cluster
similar trajectories into groups to capture the movement behaviors of the user. Finally, we
devise the clue-aware trajectory aggregation algorithm to aggregate trajectories in the same
group to derive the corresponding trajectory pattern and route. In addition, building a
trajectory profile of users for discovering community structures is presented.