Situation-aware data mining service for ubiquitous environments

Abstract

The indisputable dominance of mobile and pervasive computing devices and their typical characteristics require services offered to be rethought and sometimes redesigned in order to better assist users. Considering the importance of data mining services to provide intelligence locally on devices on these environments, we propose a data mining service that adapts the embedded data mining algorithm according to situation. Resource-awareness and context-awareness are the essential features that the proposed service will have to provide. Consequently we present a model in which data mining configuration is determined based on context and resources. We separate control and functionality in order to provide more flexibility and comply with existing data mining standards. An adaptable design is attained through definition of situations and strategies. The mechanism used in definition of strategies is an important factor affecting the performance of the control part which determines the configuration of data mining algorithm. Anticipating the importance of the mechanism selection, the paper also presents comparison with three different mechanisms. We designed a situation-aware data mining service favoring adaptability and efficiency as the important features and assessed the alternative representations of its components.