Get Knowledge Discovery from Sensor Data PDF

By Ganguly A.R., et al. (eds.)

ISBN-10: 1420082329

ISBN-13: 9781420082326

As sensors turn into ubiquitous, a collection of wide requisites is starting to emerge throughout high-priority functions together with catastrophe preparedness and administration, adaptability to weather swap, nationwide or place of origin safety, and the administration of serious infrastructures. This booklet offers cutting edge recommendations in offline information mining and real-time research of sensor or geographically disbursed facts. It discusses the demanding situations and specifications for sensor facts dependent wisdom discovery strategies in high-priority software illustrated with case stories. It explores the fusion among heterogeneous info streams from a number of sensor forms and functions in technology, engineering, and safety.

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24 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Execution Trace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Computational Complexity. . . . . . . . . . . . . . . . . . . . . . . . . 26 Basic Hotspot Detection . . . . . . . . . . . . . . . . . . . . . . .

26 Basic Hotspot Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Execution Trace . .

At first sight, it may seem unlikely that all these constraints can be satisfied simultaneously. However, we have developed a general framework for mining massive data streams that satisfies all six [5]. Within this framework, we have designed and implemented massivestream versions of decision tree induction [1,6], Bayesian network learning [5], k-means clustering [2], and the EM algorithm for mixtures of Gaussians [3]. For example, our decision tree learner, called VFDT, is able to mine on the order of a billion examples per day using off-the-shelf hardware, while providing strong guarantees that its output is very similar to that of a “batch” decision P1: Shashi October 3, 2008 13:14 82329 82329˙C002 A General Framework for Mining Massive Data Streams 11 tree learner with access to unlimited resources.

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Knowledge Discovery from Sensor Data by Ganguly A.R., et al. (eds.)


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