Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
What to do when search fails: finding information by association
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploring memory in email refinding
ACM Transactions on Information Systems (TOIS)
Social Network Extraction of Academic Researchers
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
WhozThat? evolving an ecosystem for context-aware mobile social networks
IEEE Network: The Magazine of Global Internetworking
Extraction of human social behavior from mobile phone sensing
AMT'12 Proceedings of the 8th international conference on Active Media Technology
From the internet of things to embedded intelligence
World Wide Web
Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things
Journal of Network and Computer Applications
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The ability to leverage the power of a network of social contacts is important to get things done. However, as the number of contacts increases, people often find it difficult to maintain their contact network by using merely memory, and are frequently encompassed with questions like "who is that person, I met him in Tokyo last year". Existing contact tools make up for the shortage of unreliable human memory by storing contact information in the digital format, but laying much burden on users on manually inputting contact data. This paper, however, presents a social contact management system called SCM, which supports the auto-collection of rich contact data by exploring the aggregated power of pervasive sensing and Web intelligence techniques. Regarding that people often need to leverage several associated things (e.g., meeting location) to fetch other information about a contact (e.g., his name), we also develop an associative contact retrieval method. The effectiveness and runtime performance of our system is validated through a set of experiments.