Learning from Data Streams Processing Techniques in Sensor Networks /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Gama, João (Επιμελητής έκδοσης), Gaber, Mohamed Medhat (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
• Θέματα: Computer science.; Computer communication systems.; Information storage and retrieval.; Artificial intelligence.; Electrical engineering.; Computer Science.; Information Storage and Retrieval.; Computer Communication Networks.; Signal, Image and Speech Processing.; Communications Engineering, Networks.; Artificial Intelligence (incl. Robotics).

 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. 

 Overview -- Sensor Networks: An Overview -- Data Stream Processing -- Data Stream Processing in Sensor Networks -- Data Stream Management Techniques in Sensor Networks -- Data Stream Management Systems and Architectures -- Querying of Sensor Data -- Aggregation and Summarization in Sensor Networks -- Sensory Data Monitoring -- Mining Sensor Network Data Streams -- Clustering Techniques in Sensor Networks -- Predictive Learning in Sensor Networks -- Tensor Analysis on Multi-aspect Streams -- Applications -- Knowledge Discovery from Sensor Data for Security Applications -- Knowledge Discovery from Sensor Data For Scientific Applications -- TinyOS Education with LEGO MINDSTORMS NXT.