Adapting Batch Learning Algorithms Execution in Ubiquitous Devices

In order to provide context aware, adaptive, and anticipatory services, data mining services are required to provide them with intelligence. The data mining could be either executed in a central server or locally. In either case, adaptability to the changing environment is required. In the stream mining scenario, some solutions have been proposed to provide mechanisms to adapt the execution to available resources and context. Here, we propose a cost model mechanism to adapt the algorithm execution according to available resources and context information for the case of static data. The mechanism based on analyzing efficacy and efficiency (EE-Model) of the algorithm, is a two step process in which first the efficiency and efficacy of the algorithm are calculated for predefined algorithm configurations and dataset input. In a second step, taking into account the available resources and context, the best configuration of the algorithm is chosen. The paper describes the mechanism and presents an EE-Model instantiation for C4.5 algorithm. Further, we demonstrate the convenience of the proposed approach with a simulation of synthetic data.