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Learning from Data Streams: Processing Techniques in Sensor Networks

João Gama ; Mohamed Medhat Gaber (eds.)

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Institución detectada Año de publicación Navegá Descargá Solicitá
No detectada 2007 SpringerLink

Información

Tipo de recurso:

libros

ISBN impreso

978-3-540-73678-3

ISBN electrónico

978-3-540-73679-0

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Información sobre derechos de publicación

© Springer-Verlag Berlin Heidelberg 2007

Tabla de contenidos

Introduction

João Gama; Mohamed Medhat Gaber

Part I - Overview | Pp. 1-5

Sensor Networks: An Overview

João Barros

Part I - Overview | Pp. 9-24

Data Stream Processing

João Gama; Pedro Pereira Rodrigues

Part I - Overview | Pp. 25-39

Data Stream Processing in Sensor Networks

Mohamed Medhat Gaber

Part I - Overview | Pp. 41-48

Data Stream Management Systems and Architectures

M. A. Hammad; T. M. Ghanem; W. G. Aref; A. K. Elmagarmid; M. F. Mokbel

Part II - Data Stream Management Techniques in Sensor Networks | Pp. 51-71

Querying of Sensor Data

Niki Trigoni; Alexandre Guitton; Antonios Skordylis

Part II - Data Stream Management Techniques in Sensor Networks | Pp. 73-86

Aggregation and Summarization in Sensor Networks

Nisheeth Shrivastava; Chiranjeeb Buragohain

Sensor networks generate enormous quantities of data which need to be processed in a distributed fashion to extract interesting information. We outline how ideas and algorithms from data stream query processing are revolutionizing data processing in sensor networks. We also discuss how sensor networks pose some particular problems of their own and how these are being overcome.

Part II - Data Stream Management Techniques in Sensor Networks | Pp. 87-105

Sensory Data Monitoring

Rachel Cardell-Oliver

The goal of sensory data monitoring is to maximise the quality of data gathered by a sensor network. The principal problems for this task are, specifiying which data is most relevant to user’s goals, minimising the cost of gathering that data, and clearing the gathered data. This chapter outlines the state-of-the-art in addressing each of these challenges.

Part II - Data Stream Management Techniques in Sensor Networks | Pp. 107-122

Clustering Techniques in Sensor Networks

Pedro Pereira Rodrigues; João Gama

The traditional knowledge discovery environment, where data and processing units are centralized in controlled laboratories and servers, is now completely transformed into a web of sensorial devices, some of them with local processing ability. This scenario represents a new knowledge-extraction environment, possibly not completely observable, that is much less controlled by both the human user and a common centralized control process.

Part III - Mining Sensor Network Data Streams | Pp. 125-142

Predictive Learning in Sensor Networks

João Gama; Rasmus Ulslev Pedersen

Sensor networks act in dynamic environments with distributed sources of continuous data and computing with resource constraints. Learning in these environments is faced with new challenges: the need to continuously maintain a decision model consistent with the most recent data. Desirable properties of learning algorithms include: the ability to maintain an any time model; the ability to modify the decision model whenever new information is available; the ability to forget outdated information; and the ability to detect and react to changes in the underlying process generating data, monitoring the learning process and managing the trade-off between the cost of updating a model and the benefits in performance gains. In this chapter we illustrate these ideas in two learning scenarios—centralized and distributed—and present illustrative algorithms for these contexts.

Part III - Mining Sensor Network Data Streams | Pp. 143-164