MOBILE MIND: A FULLY MOBILE PLATFORM BASED MACHINE LEARNING APPLICATION
Ahmet Selman BOZKIR
Hacettepe University Computer Engineering Department, Ankara, Turkey selmancs.hacettepe.edu.tr
Ebru AKCAPINAR SEZER
Hacettepe University Computer Engineering Department, Ankara, Turkey ebruhacettepe.edu.tr
ABSTRACT In recent years, mobile devices have developed significantly in terms of technical capabilities, computing power, storage
capacity and ability of sensing different activities via intelligent built-in sensors. In this perspective, capabilities of ultimate mobile phone technology has begun to be a candidate novel platform for machine learning and data mining
activities by the help of its computing power. In this study, a fully mobile platform based machine learning application named Mobile Mind is designed and implemented. While, all other current mobile platform based machine learning and
data mining applications are using central data mining servers to perform analysis, Mobile Mind does all tasks on cell phone’s processor and memory. On the other hand, Mobile Mind currently supports support vector regression and kernel
recursive least squares regression algorithms with polynomial and radial basis kernels to allow users performing predictive data mining operations on flat CSV comma separated values files. By this study, it is shown that mobile
platforms are becoming native and ubiquitous platforms for machine learning purposes from now on. Therefore, the need of central data mining servers and web service usage for data transferring will started to be less and less in the future.
Furthermore, a native fully mobile machine learning tool presents unlimited opportunities to the mobile application programmers especially dealing with sensor data driven applications has much potential in this point of view.
KEYWORDS
Mobile data mining, Machine learning, Support vector regression, Kernel recursive least squares
1. INTRODUCTION
Mobile data mining has started to be one the important fields of pervasive and ubiquitous mobile computing. In their study, Talia and Trunfio described the three possible usage scenarios of mobile devices in data
mining oriented applications such as 1 terminal mode which users access to a central data mining server, select data source and invoke operations on data along with a returning result; 2 data generator mode that
users or mobile devices generate data and send it to a remote server for further processing and 3 miner mode that all data mining related operations are done on device itself Talia Trunfio, 2010. However, it’s
reported that third mode is unrealistic because of limited capabilities of mobile devices such as computing power and storage area Talia Trunfio, 2010. In essence, if literature is reviewed, it can be seen that, many
of the mobile data mining studies rely on utilizing first two modes. In one study, authors designed and implemented a distributed mobile data mining environment conforming to first usage scenario depicted
above to smartly monitor and visualize stock market data on PDAs by utilizing Fourier transform of decision trees and employing Java technologies Kargupta et al., 2002. In another study, a mobile data mining
application based on communication with a remote data mining server via web service techniques is introduced Talia Trunfio, 2010. In that study, users allowed to select a data table on server and perform
data mining methods by invoking remote built-in functions of Weka Toolkit Weka, 2010. On the other hand, due to shortcomings of first and second modes in mobile data mining scenarios, an efficient mobile
data mining model is developed which helps to preprocess of local mobile data and gain descriptive statistics
before sending it to remote mining servers Goh Taniar, 2005. In another study, an intelligent system named VEDAS which tracks and monitors of vehicles by using on-board PDAs connected through wireless
networks and performs data mining analysis over recorded paths is introduced Kargupta et al., 2003. As can be seen, mobile data mining centric applications have generally avoided using the mobile device
itself for performing whole data mining task. Instead, they have used central data mining servers for analyzes and utilize web service or similar technologies for data transferring and gathering responses. This approach
has the advantage of high speed and scalable data analysis but suffers from well-known low bandwidth problem of communication lines. However, as a result of innovations in mobile device industry, current
high-end mobile devices have been equipped with adequate size of multi-touch screens, built-in magnetic field, proximity and accelerometer sensors, high resolution cameras, powerful processors up to 1 GHz, large
volumes of storage capacities up to 32 GB which eliminate the limitations over scenario three and allows mobile devices to be a powerful candidate platform for data mining machine learning activities.
Furthermore, due to rapid development of mobile devices and built-in sensors, the way of mobile application development has evolved and mobile devices have became self data generators in this case. Therefore, local
analysis of sensor generated data has much potential and valuable. One important instance of this case is motion based recognition. Liu et al., developed a single passes gesture recognition algorithm for three-axis
accelerometers which allows capturing and revealing patterns of various gestures Liu et al., 2009. As today’s mobile devices have these kinds of accelerometers, it is possible to recognize motions of users and
develop authentication mechanisms for mobile devices. However, to achieve this and similar goals, existence of mobile classifier or regressor is very essential.
In this paper, we introduce a completely mobile based machine learning tool named Mobile Mind. To allow users performing predictions in test data through employing pre-installed algorithms over train data
constitutes the main purpose of the present study. Mobile Mind currently supports support vector regression and kernel recursive least squares an online algorithm algorithms with polynomial and radial basis kernel
choices. The algorithms used in Mobile Mind are migrated from Dlib Machine Learning Toolkit King, 2009.
2. MOBILE MIND WITH ALL ASPECTS
