상세정보
학술지
A Technique for Extracting Behavioral Sequence Patterns from GPS Recorded Data
- 저자
- Thi Hong Nhan Vu, 이양구, The Duy Bui
- 발행일
- 201403
- 출처
- Computing, v.96 no.3, pp.163-188
- ISSN
- 0010-485X
- 협약과제
- 13PC1100, 5m 정밀도의 증강현실 서비스 지원 LBS 플랫폼 개발, 박상준
- 초록
- The mobile wireless market has been attracting many customers. Technically, the paradigm of anytime-anywhere connectivity raises previously unthinkable challenges, including the management of million of mobile customers, their profiles, the profiles-based selective information dissemination, and server-side computing infrastructure design issues to support such a large pool of users automatically and intelligently. In this paper, we propose a data mining technique for discovering frequent behavioral patterns from a collection of trajectories gathered by Global Positioning System. Although the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and temporal constraints on spatiotemporal sequences makes the computation feasible. Specifically, the mined patterns are incorporated with synthetic constraints, namely spatiotemporal sequence length restriction, minimum and maximum timing gap between events, time window of occurrence of the whole pattern, inclusion or exclusion event constraints, and frequent movement patterns predictive of one ore more classes. The algorithm for mining all frequent constrained patterns is named cAllMOP. Moreover, to control the density of pattern regions a clustering algorithm is exploited. The proposed method is efficient and scalable. Its efficiency is better than that of the previous algorithms AllMOP and GSP with respect to the compactness of discovered knowledge, execution time, and memory requirement. © 2013 Springer-Verlag Wien.
- KSP 제안 키워드
- Clustering algorithm, Data mining(DM), Design issues, Discovered knowledge, Infrastructure Design, Its efficiency, Mobile wireless, Movement patterns, Search Space, Sequence Patterns, Sequence length