BeTracker: A system for finding behavioral patterns from contextual sensor and social data

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

In this work, we integrate the contextual information provided from sensor data and the social relationships collected from online social networks to construct a system, termed BeTracker. We aim to find and track the frequent and representative behaviors for any user-input individual or social structural information. We claim combining physical contacts from sensor data and virtual online interactions can reveal real-life human behaviors. In our BeTracker, we mine the temporal subgraph patterns as the discovered behaviors from sensor-social data transactions. The user-given information, which is the target to observe, can be (a) an individual (to find her daily behaviors), (b) a relational structure (e.g. linear, triangle, or star structure) (to find the frequent and contextual interactions between them), and (c) a relational structure with partially assigned individuals and sequential time periods (to observe their interactions that follow certain temporal order). In the experimental part, we demonstrate promising results of different queries and present the system efficiency of the proposed behavioural pattern mining.

原文English
主出版物標題Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
頁面1227-1230
頁數4
DOIs
出版狀態Published - 2011 十二月 1
事件11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
持續時間: 2011 十二月 112011 十二月 11

出版系列

名字Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(列印)1550-4786

Other

Other11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
國家/地區Canada
城市Vancouver, BC
期間11-12-1111-12-11

All Science Journal Classification (ASJC) codes

  • 工程 (全部)

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