Catálogo de publicaciones - libros
Learning from Data Streams: Processing Techniques in Sensor Networks
João Gama ; Mohamed Medhat Gaber (eds.)
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
No disponibles.
Disponibilidad
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
2007
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
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