By Michael May, Lorenza Saitta
Knowledge discovery in ubiquitous environments is an rising quarter of analysis on the intersection of the 2 significant demanding situations of hugely allotted and cellular platforms and complicated wisdom discovery platforms. It goals to supply a unifying framework for systematically investigating the mutual dependencies of another way particularly unrelated applied sciences hired in construction next-generation clever platforms: computing device studying, info mining, sensor networks, grids, peer-to-peer networks, information movement mining, task reputation, net 2.0, privateness, consumer modelling and others. This cutting-edge survey is the end result of a big variety of workshops, summer season faculties, tutorials and dissemination occasions prepared via KDubiq (Knowledge Discovery in Ubiquitous Environments), a networking undertaking funded through the eu fee to collect researchers and practitioners of this rising neighborhood. It offers in its first half a conceptual starting place for the recent box of ubiquitous wisdom discovery - highlighting demanding situations and difficulties, and providing destiny instructions within the sector of 'smart', 'adaptive', and 'intelligent' studying. the second one a part of this quantity comprises chosen ways to ubiquitous wisdom discovery and treats particular points intimately. The contributions were conscientiously chosen to supply illustrations and in-depth discussions for a number of the significant findings of half I.
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Additional info for Ubiquitous Knowledge Discovery: Challenges, Techniques, Applications
Gridminer: An infrastructure for data mining on computational grids. In: APAC Conference and Exhibition on Advanced Computing, Grid Applications and eResearch (2003) 6. org/ 7. : Digging deep into the data mine with datamininggrid. IEEE Internet Computing (2008) 8. : Mining for misconﬁgured machines in grid systems. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 687–692 (2006) 9. : Failure diagnosis using decision trees. In: Proceedings of the International Conference on Autonomic Computing, New York, pp.
With regards to learning, meta-learning methods will be needed in order to distribute learning processes when needed and to integrate the results on a anytime basis. Since the architecture of the system may well not incorporate a global master device, these management capabilities will have themselves to be distributed and implemented as parts of the self-aware computing capacities of the individual agents. In this respect, much remains to be done, and we do not have, at this time, a mature methodology to meet that challenge.
Routing protocols usually do not aim for optimal routing – in a dynamic system the optimum can rarely be computed. Instead they are concerned with the robustness of the solution: how bad might it turn out to be with respect to reasonable scenarios, what are these worst scenarios, and how can those be avoided. The concept of robustness is not new for the knowledge discovery ﬁeld. Many of the principles developed elsewhere can and should be imported to ubiquitous knowledge discovery: Avoiding outliers, for example, can be central for eliminating the adverse eﬀects of failures.