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 realtime 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 resourceaware 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.

There is an emerging focus on real-time data stream analysis on mobile devices. While many mobile data stream mining algorithms have been developed in recent times, generic and scalable visualization techniques have not been presented. This paper presents the demonstration of our innovative clutter-adaptive cluster visualization technique for mobile devices. We have fully implemented this technique on the Google Android platform and provide demonstrations for different datasets: location (both real and synthetic), and stockmarket (real).

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