Open mobile miner: a toolkit for building situation-aware data mining applications

P. Haghighi, S. Krishnaswamy, A. Zaslavsky, M. Gaber, A. Sinha, B. Gillick

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In organizational computing and information systems, data mining techniques have been widely used for analyzing customer behaviour and discovering hidden patterns. Mobile Data Mining is the process of intelligently analysing continuous data streams on mobile devices. The use of mobile data mining for real-time business intelligence applications can be greatly advantageous. Past research has shown that resource-aware adaptation of data stream mining can significantly improve the continuity of data mining operations in mobile environments. The key underlying premise is that by varying the accuracy of the analysis process in accordance with changing available resource levels, the longevity and continuity of mobile data mining applications is ensured. In this paper we qualitatively extend the notion of resource-aware adaptation of mobile data mining to holistically enable situation-awareness feature for user applications. We then present a novel generic toolkit that enables building situation and resource-aware mobile data mining applications and describe along with underlying theoretical foundations of resource and situation criticality, awareness and adaptation which are entirely transparent and hidden from the user. The Open Mobile Miner (OMM) toolkit builds on our research for performing adaptive analysis of data streams on mobile/embedded devices. Finally, we describe a mobile health monitoring application as a case study and discuss the results of our conducted experimental evaluation which demonstrate the adaptation transparency and easy use of OMM for building mobile data mining applications such as stock market monitoring and real estate data analysis.
Original languageEnglish
Pages (from-to)224-248
Number of pages25
JournalJournal of Organizational Computing and Electronic Commerce
Issue number3
Publication statusPublished - 2013


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