Mlogger: an automatic blogging system by mobile sensing user behaviors

Authors:
Jun-Zhao Sun;Jiehan Zhou;Timo Pihlajaniemi
Affiliations:
Academy of Finland, Finland and Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland;Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland;Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland
Venue:
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Year:
2010

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Abstract

Context-awareness is the leading feature of pervasive computing. Blog is one of the first and key elements in social computing. In the emerging pervasive social computing paradigm, an interesting topic is how to blog with user behaviors automatically associated. In this paper, we present Mlogger, an automatic blogging system that can detect, recognize and track user behaviors and associate them with new blog entries. In the system, Sun SPOTs are used for sensing raw behavioral data. A Mlogger back-end system is designed to process those raw data and infer high-level user behavioral information such as "what the user is doing, and where, when, and with whom?". Associated with the inferred information, a new entry about user behaviors can be created and published automatically.