Download Learning from Data Streams: Processing Techniques in Sensor by Joao Gama, Mohamed Medhat Gaber PDF

By Joao Gama, Mohamed Medhat Gaber

Sensor networks encompass disbursed independent units that cooperatively display screen an atmosphere. Sensors are outfitted with capacities to shop details in reminiscence, method this knowledge and speak with their friends. Processing facts streams generated from instant sensor networks has raised new learn demanding situations during the last few years as a result of large numbers of knowledge streams to be controlled consistently and at a really excessive rate.The publication presents the reader with a finished assessment of flow info processing, together with recognized prototype implementations just like the Nile approach and the TinyOS working process. The set of chapters covers the state-of-art in info circulation mining methods utilizing clustering, predictive studying, and tensor research thoughts, and using them to functions in safety, the traditional sciences, and education.This study monograph supplies to researchers and graduate scholars the cutting-edge in information flow processing in sensor networks. the massive bibliography bargains an outstanding place to begin for extra interpreting and destiny examine.

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IEEE/ACM Transactions on Networks, 11(1):2–16, 2003. [15] J. Körner, K. Marton, How to encode the modulo 2 sum of two binary sources. IEEE Transactions on Information Theory, IT-25:219–221, 1979. R. Kschischang, B. -A. Loeliger, Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47(2):498–519, 2001. [17] P. Levis, D. Culler, Maté: a tiny virtual machine for sensor networks. SIGOPS Operating Systems Review, 36(5):85–95, 2002. [18] S. S. Raghavendra, Pegasis: power-efficient gathering in sensor information systems.

Abadi, D. Carney, U. Cetintemel, M. Cherniack, C. Convey, C. Erwin, E. Galvez, M. Hatoun, J. Hwang, A. Maskey, A. Rasin, A. Singer, M. Stonebraker, N. Tatbul, Y. Xing, R. Yan, S. Zdonik, Aurora: a data stream management system (demonstration). In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’03), San Diego, CA, June 2003. [2] A. Arasu, B. Babcock, S. Babu, M. Datar, K. Ito, I. Nishizawa, J. Rosenstein, J. Widom, STREAM: The Stanford stream data manager demonstration description—short overview of system status and plans.

Traditional data stream mining techniques have paid limited attention to this problem due to reasonable availability of computational resources to mine data streams in high-performance computational facilities. On the other hand and due to in-network processing requirements, it is a significant factor for data mining in sensor networks. Having discussed the two classes of data stream processing in sensor networks, the following section discusses the research issues and challenges that face data management and mining in sensor networks.

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