Gama Learning from Data Streams

Processing Techniques in Sensor Networks
1. Auflage 2007
ISBN: 978-3-540-73679-0
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 244 Seiten

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.

Autoren/Hrsg.

Gama, João

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;Chapter 1 Introduction;11
3.1;1.1 Preamble;11
3.2;1.2 Book Overview;12
3.3;1.3 Roadmap;13
3.4;1.4 Final Remarks;14
3.5;References;14
4;Part I Overview;16
4.1;Chapter 2 Sensor Networks: An Overview;17
4.1.1;2.1 Sensing and Communicating;17
4.1.2;2.2 Current Sensor Network Technology;19
4.1.3;2.3 Communication Aspects;21
4.1.4;2.4 Distributed Compression and In-Network Computation;25
4.1.5;2.5 Summary and Concluding Remarks;30
4.2;Chapter 3 Data Stream Processing;33
4.2.1;3.1 Introduction;33
4.2.2;3.2 Data Stream Models;34
4.2.3;3.3 Basic Streaming Methods;36
4.2.4;3.4 Basic Streaming Algorithms;41
4.2.5;3.5 Emerging Challenges and Future Issues;45
4.2.6;References;46
4.3;Chapter 4 Data Stream Processing in Sensor Networks;48
4.3.1;4.1 Introduction;48
4.3.2;4.2 Classification of Data Stream Processing in Sensor Networks;49
4.3.3;4.3 Research Issues and Challenges;51
4.3.4;4.4 Summary;52
4.3.5;References;53
5;Part II Data Stream Management Techniques in Sensor Networks;56
5.1;Chapter 5 Data Stream Management Systems and Architectures;57
5.1.1;5.1 Introduction;57
5.1.2;5.2 Nile System Design;59
5.1.3;5.3 The Correctness Measure;63
5.1.4;5.4 The Progressive Evaluation of Sliding-Window Queries;64
5.1.5;5.5 Extensions;69
5.1.6;5.6 A Scalable Data Stream Management System;73
5.1.7;5.7 RelatedWork;75
5.1.8;5.8 Summary;76
5.1.9;References;76
5.2;Chapter 6 Querying of Sensor Data;78
5.2.1;6.1 Introduction;78
5.2.2;6.2 Types of Queries;79
5.2.3;6.3 Query Dissemination;81
5.2.4;6.4 Result Collection;84
5.2.5;6.5 Data-Centric Storage;88
5.2.6;6.6 Concluding Remarks;89
5.2.7;References;89
5.3;Chapter 7 Aggregation and Summarization in Sensor Networks;92
5.3.1;7.1 Introduction;92
5.3.2;7.2 Preliminaries and RelatedWork;95
5.3.3;7.3 Complex Queries in Sensor Networks;98
5.3.4;7.4 Aggregation in Lossy Networks;105
5.3.5;7.5 Conclusion and Future Directions;108
5.3.6;References;108
5.4;Chapter 8 Sensory Data Monitoring;111
5.4.1;8.1 Specifying Sensory Data Monitoring Goals;113
5.4.2;8.2 Identifying Significant Data: In-network processing;117
5.4.3;8.3 Accuracy: Identifying Sensing Errors;120
5.4.4;8.4 Summary;123
5.4.5;References;124
6;Part III Mining Sensor Network Data Streams;127
6.1;Chapter 9 Clustering Techniques in Sensor Networks;128
6.1.1;9.1 A Ubiquitous Streaming Setting;128
6.1.2;9.2 The Core of Clustering Procedures;129
6.1.3;9.3 Clustering Streaming Examples;132
6.1.4;9.4 Clustering Multiple Data Streams;135
6.1.5;9.5 Open Issues on Clustering Sensor Data Streams;142
6.1.6;References;143
6.2;Chapter 10 Predictive Learning in Sensor Networks;146
6.2.1;10.1 Introduction;146
6.2.2;10.2 General Issues;147
6.2.3;10.3 Centralized Approaches;154
6.2.4;10.4 Distributed Solutions;163
6.2.5;References;166
6.3;Chapter 11 Tensor Analysis on Multi-aspect Streams;168
6.3.1;11.1 Introduction;168
6.3.2;11.1 Introduction;168
6.3.3;11.2 Background;172
6.3.4;11.3 Problem Formulation;175
6.3.5;11.4 Window-Based Tensor Analysis;176
6.3.6;11.5 Performance Evaluation;182
6.3.7;11.6 Application and Case Study;183
6.3.8;11.7 RelatedWork;185
6.3.9;11.8 Conclusion;186
7;Part IV Applications;188
7.1;Chapter 12 Knowledge Discovery from Sensor Data for Security Applications;189
7.1.1;12.1 Introduction;189
7.1.2;12.2 Security Challenges;190
7.1.3;12.3 Disparate Data Exploitation;191
7.1.4;12.4 Case Study: Weigh Station Sensors;194
7.1.5;12.5 Closing Remarks;203
7.2;Chapter 13 Knowledge Discovery from Sensor Data For Scientific Applications;207
7.2.1;13.1 Introduction;208
7.2.2;13.2 A Broader Knowledge Discovery Framework;209
7.2.3;13.3 Weather, Climate, and Associated Natural Hazards;212
7.2.4;13.4 Challenges of Knowledge Discovery from Sensor Data for Natural Hazards;216
7.2.5;13.5 Knowledge Discovery Approaches forWeather, Climate and Associated Natural Hazards;219
7.2.6;13.6 The Significance of Utilizing Knowledge Discovery Insights for Hazards Mitigation;225
7.2.7;13.7 Closing Remarks;227
7.2.8;References;228
7.3;Chapter 14 TinyOS Education with LEGO MINDSTORMS NXT;232
7.3.1;14.1 Introduction;232
7.3.2;14.2 NXT Brick;233
7.3.3;14.3 LEGO Ecosystem;238
7.3.4;14.4 Proposing a TinyOS Educational Platform for NXT;241
7.3.5;14.5 Conclusion;241
8;Index;243