Energy Characterization of Data Mining Algorithms on Mobile Devices

Abstract

The pervasive availability of increasingly powerful mobile computing devices like PDAs, smartphones and wearable sensors, is widening their use in complex applications such as collaborative analysis, information sharing, and data mining in a mobile context. A key aspect to be addressed to enable effective and reliable data mining over mobile devices is ensuring energy efficiency. In particular, energy characterization plays a critical role in determining the requirements of data-intensive applications that can be efficiently executed over mobile devices (e.g., PDA-based monitoring, event management in sensor networks). Therefore, there is an increasing need to understand the bottlenecks associated with the execution of these applications in modern mobile-based architectures. This paper presents an experimental study of the energy consumption behaviour of representative data mining algorithms running on mobile devices. Specifically, we consider algorithms for association rule mining, clustering, and decision tree induction. Our study reveals that, although data mining algorithms are compute- and memory-intensive, by appropriate tuning of a few parameters associated to data (e.g., data set size, number of attributes, size of produced results) those algorithms can be efficiently executed on mobile devices by saving energy and, thus, prolonging devices lifetime.