TY - GEN
T1 - Mobile Mind
T2 - IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011
AU - Bozkir, Ahmet Selman
AU - Sezer, Ebru Akcapinar
PY - 2011
Y1 - 2011
N2 - 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 have begun to make mobile systems to be a candidate novel platform for machine learning and data mining activities. 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 for 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.
AB - 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 have begun to make mobile systems to be a candidate novel platform for machine learning and data mining activities. 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 for 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.
KW - Kernel recursive least squares
KW - Machine learning
KW - Mobile data mining
KW - Support vector regression
UR - https://www.scopus.com/pages/publications/84865099883
M3 - Conference contribution
AN - SCOPUS:84865099883
SN - 9789728939533
T3 - Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011
SP - 95
EP - 101
BT - Proceedings of the IADIS European Conference on Data Mining 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011
Y2 - 24 July 2011 through 26 July 2011
ER -