Mobile Data Mining-Based Services on the Base of Mobile Device Management (MDM) System

Mazin Omar Khairo

Management of Information Technology Department, Umm Al-Qura University, Mecca, KSA.

DOI: 10.4236/jsip.2014.53011                

Abstract

Client software on mobile devices that can cause the remote control perform data mining tasks and show production results is significantly added the value for the nomadic users and organizations that need to perform data analysis stored in the repository, far away from the site, where users work, allowing them to generate knowledge regardless of their physical location. This paper presents new data analysis methods and new ways to detect people work location via mobile computing technology. The growing number of applications, content, and data can be accessed from a wide range of devices. It becomes necessary to introduce a centralized mobile device management. MDM is a KDE software package working with enterprise systems using mobile devices. The paper discussed the design system in detail.

Keywords

Data Mining, Mobile Device Management, User Recommendation, KDE Software Package

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Lennon, R.G. (2012) Bring Your Own Device (BYOD) with Cloud 4 Education. Proceedings of the 3rd Annual Conference on Systems, Programming and Applications: Software for Humanity, New York, 171-180.
[2]	Wilson, S. and McCarthy, G. (2010) The Mobile University: From the Library to the Campus. Reference Services Review, 38, 214-225.
[3]	Gartner IT-Glossary. http://www.gartner.com/it-glossary/mobile-device-manage-mentmdm/
[4]	Hristidis, V., Chen, S., Li, T., Luis, S. and Deng, Y. (2010) Survey of Data Management and Analysis in Disaster Situations. The Journal of Systems and Software, 83, 1701-1714. http://dx.doi.org/10.1016/j.jss.2010.04.065
[5]	Fielding, R.T. (2000) Architectural Styles and the Design of Network-Based Software Architectures. Doctoral Dissertation, University of California, Irvine.
[6]	Zheng, L., Shen, C., Tang, L., Li, T., Luis, S., Chen, S. and Hristidis, V. (2010) Using Data Mining Techniques to Address Critical Information Exchange Needs in Disaster Affected Public-Private Networks. KDD’10, 125-134.
[7]	Choudhury, M.D., Mason, W.A., Hofman, J.M. and Watts, D.J. (2010) Inferring Relevant Social Networks from Interpersonal Communication. Proceedings of the 19th International Conference on World Wide Web, Raleigh, 26-30 April 2010, 10 p.
[8]	Kahanda, I. and Neville, J. (2009) Using Transactional Information to Predict Link Strength in Online Social Networks. Proceedings of the 3rd International Conference on Weblogs and Social Media (ICWSM), West Lafayette, June 2009, 74-81.
[9]	Ye, Y., et al. (2009) Mining Individual Life Pattern Based on Location History. 10th International Conference on Mobile Data Management: Systems, Services and Middleware, MDM’09, Taipei, 18-20 May 2009, 1-10.
[10]	Bao, T.F., et al. (2012) Mining Significant Places from Cell ID Trajectories: A Geo-Grid Based Approach. 2012 IEEE 13th International Conference on Mobile Data Management (MDM), Bengaluru, 23-26 July 2012, 288-293.
[11]	Lu, J.L. and Sun, G.J. (2012) Location-Based Intelligent Services of Scenic Areas. 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), Yichang, 21-23 April 2012, 1882-1885.
[12]	Emery, S. (2012) Factors for Consideration When Developing a Bring Your Own Device (BYOD) Strategy in Higher Education. http://hdl.handle.net/1794/12254
[13]	Apple Inc. (2014) Location Awareness Programming. http://developer.apple.com/library/ios/documentation/UserExperience/Conceptual/Location AwarenessPG/LocationAwarenessPG.pdf