Situation-Aware adaptive visualization for sensory data stream mining

Authors:
Pari Delir Haghighi;Brett Gillick;Shonali Krishnaswamy;Mohamed Medhat Gaber;Arkady Zaslavsky
Affiliations:
Centre for Distributed Systems and Software Engineering, Monash University, Caulfield East, VIC, Australia;Centre for Distributed Systems and Software Engineering, Monash University, Caulfield East, VIC, Australia;Centre for Distributed Systems and Software Engineering, Monash University, Caulfield East, VIC, Australia;Centre for Distributed Systems and Software Engineering, Monash University, Caulfield East, VIC, Australia;Centre for Distributed Systems and Software Engineering, Monash University, Caulfield East, VIC, Australia
Venue:
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Year:
2008

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Abstract

With the emergence of ubiquitous data mining and recent advances in mobile communications, there is a need for visualization techniques to enhance the user-interactions, real-time decision making and comprehension of the results of mining algorithms. In this paper we propose a novel architecture for situation-aware adaptive visualization that applies intelligent visualization techniques to data stream mining of sensory data. The proposed architecture incorporates fuzzy logic principles for modeling and reasoning about context/situations and performs gradual adaptation of data mining and visualization parameters according to the occurring situations. A prototype of the architecture is implemented and described in the paper through a real-world scenario in the area of healthcare monitoring.