Tutorial: Advances in Data Stream Mining for Mobile and Ubiquitous Environments
Joao Gama - University of Porto
Shonali Krishnaswamy - Monash University
Mohamed Gaber - University of Portsmouth)
Data streams is a topic born in databases and data warehousing communities that poses new challenges and problems for Data Mining and Machine Learning researchers. This tutorial will present the emerging state-of-the-art in developing the next generation of mobile and ubiquitous data stream processing algorithms, systems and applications. Basic knowledge of databases and data mining is assumed. The scope of the tutorial includes background theory of data stream mining, theoretical foundations of mobile/ubiquitous data stream mining and explanation of the algorithms that are the current state-of-the-art, as well as demonstration of real-world application case studies using the first integrated mobile data stream mining toolkit (Open Mobile Miner) which has been demonstrated at both KDD 2009 and ICDM 2010.
The tutorial presents the fundamental techniques for data stream analysis such as change detection, clustering, classification, frequent patterns, and time series analysis from distributed data streams. The critical factors which need to be considered in order to develop and deploy data stream mining in mobile/ubiquitous environments including the need for adaptation and context/situation-aware reasoning will be detailed. This discussion is followed by a presentation of the state-of-the-art algorithms for mobile data stream mining. The tutorial will also present the Open Mobile Miner (OMM) toolkit for rapid deployment of mobile data stream mining and real-world application/case studies and demonstrations in the areas of Intelligent Transportation Systems, Patient Monitoring and Habitat Monitoring to stimulate the real need for this growing research field. Finally the tutorial will be concluded with open issues and future directions. The tutorial also provides a substantial list of data stream mining resources.
The key learning objectives of the tutorial are to enable understanding of the motivations, rationale and challenges of the emerging important area of data stream mining in mobile and ubiquitous environments, and in-depth knowledge of techniques for mobile/ubiquitous data stream mining and identification of the key research and application challenges in this domain.
Speakers:
Joao Gama - University of Porto
Joao Gama is a researcher at LIAAD, the Laboratory of Artificial Intelligence and Computer Science of the University of Porto, working at the Machine Learning group. His main research interest is in Learning from Data Streams. He has published several articles in change detection, learning decision trees from data streams, hierarchical Clustering from streams, etc. Editor of special issues on Data Streams in Intelligent Data Analysis, J. Universal Computer Science, and New Generation Computing Co-chair of a series of Workshops on Knowledge Discovery in Data Streams, ECML 2004, Pisa, Italy, ECML 2005, Porto, Portugal, ICML 2006, Pittsburgh, US, ECML 2006 Berlin, Germany, SAC2007, Korea, SAC08, Brazil. Editor (with M Gaber) of the book Learning from Data Streams -- Processing Techniques in Sensor Networks, Springer, 2007.
Shonali Krishnaswamy - Monash University
Shonali Krishnaswamy is an Associate Professor in the Faculty of Information Technology and Director of the Centre for Distributed Systems and Software Engineering at Monash University, Australia. Her research interests include distributed data mining, data stream mining in mobile and embedded environments and web services/service oriented computing (where her focus is on quality of service and reputation models). She has authored over 100 research publications with the following bibliometrics: H-Index: 16, G-Index: 30, Citations: over 1300. Shonali has been the recipient of the following awards since commencing her academic career in 2003: Monash University Vice-Chancellors Award for Excellence in Research by an Early Career Researcher, IBM Innovation Award (UIMA), Faculty of Information Technology Early Career Researcher Award and an Australian Post Doctoral Fellowship (2003-2005) from the Australian Research Council. Shonali has linkages and partnerships with IBM Research Labs, Centre for Accident Research and Road Safety Queensland, Insurance Australia Group, the Department of Primary Industries, and the Cardiovascular Research Unit (Monash University) which involve the deployment of ubiquitous data stream mining algorithms in these application domains.
Mohamed Gaber - University of Portsmouth)
Mohamed Medhat Gaber is a Senior Lecturer at the University of Portsmouth, England, UK. Prior to this he held an Australian Research Council (ARC) sponsored Australian Post Doctorial (APD) Fellowship at Monash University, Australia and was a Research Scientist at CSIRO Australia. He has published more than 70 research papers. Mohamed is the co-editor (with J. Gama) of the book: Learning from Data Streams: Processing Techniques in Sensor Networks published by Springer in 2007 and the book: Knowledge Discovery from Sensor Data by CRC published in 2008. He is the sole editor of the book Scientific Data Mining and Knowledge Discovery: Principles and Foundations, published by Springer in 2009. Mohamed has served in the program committees of many international and local conferences and workshops in the area of data mining and context-aware computing. He was the co-chair of the IEEE International Workshop on Mining Evolving and Streaming Data in conjunction with ICDM 2006, International Workshop on Knowledge Discovery from Ubiquitous Data Streams held in conjunction with ECML/PKDD 2007, and the First, Second, Third, Fourth and Fifth International Workshop on Knowledge Discovery from Sensor Data held in conjunction with ACM SIGKDD 2007-2011. He has recently co-chaired the Workshop on Learning from Medical Data Streams (LEMEDS'11), in conjunction with the 13th Conference on Artificial Intelligence in Medicine 2011.
