Learning from Data Streams

Processing Techniques in Sensor Networks

By João Gama , Mohamed Medhat Gaber

Learning from Data Streams Cover Image

  • ISBN13: 978-3-5407-3678-3
  • 254 Pages
  • User Level: Science
  • Publication Date: September 20, 2007
  • Available eBook Formats: PDF
  • eBook Price: $109.00
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Full Description
Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment. Sensors are equipped with capacities to store information in memory, process this information and communicate with their neighbors. Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate. The 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. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education. This research monograph delivers to researchers and graduate students the state of the art in data stream processing in sensor networks. The huge bibliography offers an excellent starting point for further reading and future research.
Table of Contents

Table of Contents

  1. 1 Introduction (Gama, Gaber).
  2. Part I: Overview: 2 Sensor Networks (Barros)
  3. 3 Data Stream Processing (Gama, Rodrigues)
  4. 4 Data Stream Processing in Sensor Networks (Gaber).
  5. Part II: Data Stream Management Techniques in Sensor Networks: 5 Data Stream Management Systems and Architectures (Hammad, Ghanem, Aref, Elmagarmid, Mokbel)
  6. 6 Querying of Sensor Data (Trigoni, Guitton, Skordylis)
  7. 7 Aggregation and Summarization in Sensor Networks (Shrivastava, Buragohain)
  8. 8 Sensory Data Monitoring (Cardell
  9. Oliver).
  10. Part III: Mining Sensor Network Data Streams: 9 Clustering Techniques in Sensor Networks (Rodrigues, Gama)
  11. 10 Predictive Learning in Sensor Networks (Gama, Pedersen)
  12. 11 Tensor Analysis on Multi
  13. aspect Streams (Sun, Papdimitriou, Yu).
  14. Part IV: Applications: 12 Knowledge Discovery from Sensor Data for Security Applications (Ganguly, Omitaomu, Walker)
  15. 13 Knowledge Discovery from Sensor Data for Scientific Applications (Ganguly, Fang, Khan, Omitaomu)
  16. 14 TinyOS Education with LEGO MINDSTORMS NXT (Pedersen).
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