Situation-Aware Data Stream Mining Service for Ubiquitous Applications

Advances in data mining, particularly in anytime anywhere data stream mining, make on-board data analysis possible in mobile devices with resource constraints. In this work, we propose a data stream mining service to support knowledge discovery in ubiquitous applications while addressing resource constraints on mobile devices. As the basis for our service we describe a general mechanism, which autonomously adapts the execution of the data stream mining process to each situation, using context and resource awareness. We describe the main components to achieve adaptability and propose a decision mechanism based on machine learning to support the configuration selection task, as we consider this to be a key element to achieve autonomy and adaptation of the mining service. We then present an instantiation of the proposed approach for the particular case of classification using the VFDT algorithm and analyze which factors influence it. Experimental results show how the adaptable data stream mining service improves resource consumption while increasing the quality of the anytime mining model.