Open Mobile Miner: A Toolkit for Building Situation-Aware Data Mining Applications

In organizational computing and information systems, data mining techniques have been widely used for analyzing customer behavior and discovering hidden patterns. Mobile Data Mining is the process of intelligently analyzing 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 article 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.

[1]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[2]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[3]  Mohamed Medhat Gaber,et al.  Situation-Aware Adaptive Processing (SAAP) of Data Streams , 2010, Pervasive Computing, Innovations in Intelligent Multimedia and Applications.

[4]  Philip S. Yu,et al.  A Holistic Approach for Resource-aware Adaptive Data Stream Mining , 2006, New Generation Computing.

[5]  Mohamed Medhat Gaber,et al.  Clutter-Adaptive Visualization for Mobile Data Mining , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[6]  Mohamed Medhat Gaber,et al.  Resource-aware Very Fast K-Means for ubiquitous data stream mining , 2005 .

[7]  Mohamed Medhat Gaber,et al.  Data Stream Mining Using Granularity-Based Approach , 2009, Foundations of Computational Intelligence.

[8]  Mohamed Medhat Gaber,et al.  Visualisation of Fuzzy Classification of Data Elements in Ubiquitous Data Stream Mining , 2006, IWUC.

[9]  Mohamed Medhat Gaber,et al.  Open Mobile Miner: a toolkit for mobile data stream mining , 2009, KDD 2009.

[10]  Juan-Luis Gorricho,et al.  Activity Recognition from Accelerometer Data on a Mobile Phone , 2009, IWANN.

[11]  Mohamed Medhat Gaber,et al.  On-board Mining of Data Streams in Sensor Networks , 2005 .

[12]  Thomas Seidl,et al.  MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering , 2010, WAPA.

[13]  Lei Liu,et al.  MobiMine: monitoring the stock market from a PDA , 2002, SKDD.

[14]  Mohamed Medhat Gaber,et al.  Ubiquitous data stream mining , 2004 .

[15]  Hillol Kargupta,et al.  MineFleet®: The Vehicle Data Stream Mining System for Ubiquitous Environments , 2010, Ubiquitous Knowledge Discovery.

[16]  Michael J. Shaw,et al.  Using Inductive Learning to Predict Bankruptcy , 2002, J. Organ. Comput. Electron. Commer..

[17]  Zhanpeng Jin,et al.  Predicting cardiovascular disease from real-time electrocardiographic monitoring: An adaptive machine learning approach on a cell phone , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[19]  Arantza Illarramendi,et al.  Real-time classification of ECGs on a PDA , 2005, IEEE Transactions on Information Technology in Biomedicine.

[20]  Mohamed Medhat Gaber,et al.  Adaptive Clutter-Aware Visualization for Mobile Data Stream Mining , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[21]  Geoff Hulten,et al.  A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering , 2001, ICML.

[22]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[23]  Arkady B. Zaslavsky,et al.  Towards a theory of context spaces , 2004, IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second.

[24]  Xi Chen,et al.  Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn , 2009, J. Organ. Comput. Electron. Commer..

[25]  Peter Hannay,et al.  GeoIntelligence: Data Mining Locational Social Media Content for Profiling and Information Gathering , 2011 .

[26]  Valérie Gay,et al.  User Adoption of Mobile Apps for Chronic Disease Management: A Case Study Based on myFitnessCompanion® , 2012, ICOST.

[27]  Philip S. Yu,et al.  Homogeneous and Heterogeneous Distributed Classification for Pocket Data Mining , 2012, Trans. Large Scale Data Knowl. Centered Syst..

[28]  Luca Benini,et al.  Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection , 2008, EWSN.

[29]  Mohamed Medhat Gaber,et al.  Context-aware adaptive data stream mining , 2009, Intell. Data Anal..

[30]  Mohamed Medhat Gaber,et al.  Reasoning about Context in Uncertain Pervasive Computing Environments , 2008, EuroSSC.

[31]  Tak-Chung Fu,et al.  Representing financial time series based on data point importance , 2008, Eng. Appl. Artif. Intell..

[32]  Kun Liu,et al.  VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring , 2004, SDM.

[33]  Paul Lukowicz,et al.  Power and accuracy trade-offs in sound-based context recognition systems , 2007, Pervasive Mob. Comput..

[34]  Laurence T. Yang.  Mobile Data Mining on Small Devices through Web Services , 2010 .

[35]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[36]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[37]  Mani Srivastava,et al.  Energy-aware wireless microsensor networks , 2002, IEEE Signal Process. Mag..

[38]  Timothy W. Finin,et al.  On data management in pervasive computing environments , 2004, IEEE Transactions on Knowledge and Data Engineering.

[39]  Mohamed Medhat Gaber,et al.  Resource-aware Online Data Mining in Wireless Sensor Networks , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.