Titlebook: Ubiquitous Knowledge Discovery; Challenges, Techniqu Michael May,Lorenza Saitta Book 2010 The Editor(s) (if applicable) and The Author(s),

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Privacy and Security in Ubiquitous Knowledge Discoverye that ensuring privacy and security is a big challenge in ubiquitous computing (UbiComp). This is due to the fact that ubiquitous computing is highly integrated into our daily lives, making the dependability of these systems ever more central and yet difficult to control.

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On-Line Learning: Where Are We So Far?s involving data flows, the detection of, or adaption to, changing conditions and long-life learning. On the other hand, it is now apparent that the current statistical theory of learning, based on the independent and stationary distribution assumption, has reached its limits and must be completed o

Introduction

Change Detection with Kalman Filter and CUSUMalgorithms is the ability of incremental incorporating new data in the actual decision model. Several incremental learning algorithms have been proposed. However most of them make the assumption that the examples are drawn from a stationary distribution [14]. The aim of this study is to present a de

insomnia

A Geometric Approach to Monitoring Threshold Functions over Distributed Data Streamsring simple aggregated values, such as the frequency of appearance of items in the streams. More involved challenges, such as the important task of feature selection (e.g., by monitoring the information gain of various features), still require very high communication overhead using naive, centralize

含水層

Privacy Preserving Spatio-temporal Clustering on Horizontally Partitioned Dataemporal data and, by its nature, spatio-temporal data sets, when they describe the movement behavior of individuals, are highly privacy sensitive. In this chapter, we propose a privacy preserving spatio-temporal clustering method for horizontally partitioned data. Our methods are based on building t

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