Implementation and Performance Analysis of Data-Mining Classification Algorithms on Smartphones
Smartphones are increasingly being used to capture data and perform complex tasks, however, this rarely extends to the local training of data models. This study investigates the implementation of data mining classification algorithms on smartphones, using 20 classifiers and nine mixed-design datasets on three devices. Their accuracy and processing speed are further compared against a laptop computer using our cross-platform ‘DataBench’ software. Results show that smartphones not only deliver classification accuracy equal to that of more powerful computers when using the same algorithms, as expected, but also, as many as 75% of the 180 algorithm/dataset learning tasks tested were completed on smartphone hardware within three seconds. However, tests further show that the increased complexity of newer algorithms searching for ever greater classification accuracy is resulting in model-build times growing at an exponential rate. Additional testing identified that while a single algorithm execution can have negligible effect on battery life, power efficiency is affected by algorithm complexity, data size and attribute type. The increased processing demand of local model-learning on smartphones also results in increased power dissipation. Yet, even on a continuous-loop execution basis, mobile temperature gains over a 15-min period did not exceed 7 °C. Our conclusion is that smartphones are ready to form self-reliant mobile data-mining solutions able to efficiently execute a wide range of classification algorithms. This offers numerous advantages, including data security and privacy improvements, removal of reliance on network connectivity and delivery of personalised learning.
KeywordsBattery life Classification Data mining Smartphones
This research is supported by an Australian Government Research Training Program (RTP) scholarship.
- 2.Pongnumkul, S., Chaovalit, P., Surasvadi, N.: Applications of smartphone-based sensors in agriculture: a systematic review of research. J. Sens. 2015 (2015)Google Scholar
- 3.Nath, S.: ACE: exploiting correlation for energy-efficient and continuous context sensing. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services. ACM (2012)Google Scholar
- 4.Srinivasan, V., Moghaddam, S., Mukherji, A., Rachuri, K.K., Xu, C., Tapia, E.M.: Mobileminer: mining your frequent patterns on your phone. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM (2014)Google Scholar
- 5.BinDhim, N.F., Shaman, A.M., Trevena, L., Basyouni, M.H., Pont, L.G., Alhawassi, T.M.: Depression screening via a smartphone app: cross-country user characteristics and feasibility. J. Am. Med. Inform. Assoc. 22(1), 29–34 (2015)Google Scholar
- 6.Raihan, M., et al.: Smartphone based ischemic heart disease (heart attack) risk prediction using clinical data and data mining approaches, a prototype design. In: 2016 19th International Conference on Computer and Information Technology (ICCIT). IEEE (2016)Google Scholar
- 7.Kose, M., Incel, O.D., Ersoy, C.: Online human activity recognition on smart phones. In: Workshop on Mobile Sensing: From Smartphones and Wearables to Big Data (2012)Google Scholar
- 8.Weka 3: Data Mining Software in Java. http://www.cs.waikato.ac.nz/ml/weka/. Accessed 5 Aug 2018
- 9.Dheeru, D., Karra Taniskidou, E.: UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets.html. Accessed 12 Aug 2018