Ubiquitous Knowledge Discovery Challenges, Techniques, Applications
Knowledge discovery in ubiquitous environments is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. It aims to provide a unifying framework for s
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Subseries of Lecture Notes in Computer Science
6202
Michael May Lorenza Saitta (Eds.)
Ubiquitous Knowledge Discovery Challenges, Techniques, Applications
13
Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany Volume Editors Michael May Fraunhofer IAIS Schloss Birlinghoven, 53754 Sankt Augustin, Germany E-mail: [email protected] Lorenza Saitta Università del Piemonte Orientale Amedeo Avogadro Dipartimento di Informatica Viale Teresa Michel 11, 13100, Alessandria, Italy E-mail: [email protected]
Library of Congress Control Number: 2010936640
CR Subject Classification (1998): I.2.4, I.2, H.3-4, C.2, F.1, H.2.8 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-642-16391-2 Springer Berlin Heidelberg New York 978-3-642-16391-3 Springer Berlin Heidelberg New York
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Preface
Over the last years, ubiquitous computing has started to create a new world of small, heterogeneous, and distributed devices that have the ability to sense, to communicate and interact in ad hoc or sensor networks and peer-to-peer systems. These large-scale distributed systems, in many cases, have to interact in real-time with their users. Knowledge discovery in ubiquitous environments (KDubiq) is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. It aims to provide a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, peer-to-peer networks, data stream mining, activity recognition, Web 2.0, privacy, user modeling and others. In a fully ubiquitous setting, the learning typically takes place in situ, inside the small devices. Its characteristics are quite different from currently mainstream data mining and machine learning. Instead of offline-learning in a batch setting, sequential learning, anytime learning, real-time learning, online learning, etc.—under real-time constraints from ubiqu
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