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a generic local algorithm for mining data streams in large distributed systems

A Generic Local Algorithm for Mining Data Streams in

A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Abstract: In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system.

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A Generic Local Algorithm for Mining Data Streams in

A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Ran Wolff, Kanishka Bhaduri, and Hillol Kargupta Senior Member, IEEE Abstract—In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system’s functionality

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A Generic Local Algorithm for Mining Data Streams in

A Generic Local Algorithm for Mining Data Streams in Large Distrib uted Systems Ran W olff, Kanishka Bhaduri, and Hillol Kargupta Senior Member, IEEE Abstract — In a large network of computers or...

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A Generic Local Algorithm for Mining Data Streams in

A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems

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A Generic Local Algorithm for Mining Data Streams in

A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Ran Wolff, Kanishka Bhaduri, and Hillol Kargupta Senior Member, IEEE Abstract— In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system’s functionality

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A Generic Local Algorithm for Mining Data Streams in

A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems . Shared by Kanishka Bhaduri, updated on Sep 22, 2010 Summary . Author(s) : R. Wolff, Kanishka Bhaduri, H. Kargupta Abstract . In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's

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1A Generic Local Algorithm for Mining Data Streams in

1A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems . By Ran Wolff, Kanishka Bhaduri, Hillol Kargupta and Senior Member. Abstract. Abstract — In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system’s functionality such as message routing, information

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A Generic Local Algorithm for Mining Data Streams in

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A Generic Local Algorithm for Mining Data Streams in

Computing global data mining models e.g. decision trees, k-means clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost, which may be high. The cost further increases in a dynamic scenario when the data changes rapidly. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient \emph{local} algorithm

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A Generic Local Algorithm for Mining Data Streams in

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A Generic Local Algorithm for Mining Data Streams in

A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Autorzy. Wolff, Bhaduri, Kargupta. Treść / Zawartość. Warianty tytułu. Języki publikacji. Abstrakty. In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing

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[PDF] Towards Data Mining in Large and Fully

Corpus ID: 17992662. Towards Data Mining in Large and Fully Distributed Peer-to-Peer Overlay Networks @inproceedings{WojtekKowalczyk2003TowardsDM, title={Towards Data Mining in Large and Fully Distributed Peer-to-Peer Overlay Networks}, author={M. WojtekKowalczyk and Jelasity A. E. Eiben}, year={2003} }

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UMBC Dr. Hillol Kargupta

A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Transactions on Knowledge and Data Engineering. Volume 21, Issue 4, pp. 465-478. April 2009. IEEE Transactions on Knowledge and Data Engineering.

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DPSP: Distributed Progressive Sequential Pattern Mining

21/06/2010· Wolff, R., Bhaduri, K., Kargupta, H.: A generic local algorithm for mining data streams in large distributed systems. IEEE Trans. on Knowledge and Data Engineering 21(4), 465–478 (2009) CrossRef Google Scholar

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A Very Fast Decision Tree Algorithm for Real-Time Data

The data gathered by distributed sensors—which serve as the eyes and ears of the system—are delivered to a decision center or a gateway sensor node that interprets situational information from the data streams. Although many other machine learning techniques have been extensively studied, real-time data mining of high-speed and nonstationary data streams represents one of the most

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Distributed Convex Thresholding Proceedings of the

R. Wolff, K. Bhaduri, and H. Kargupta. Local L2 Thresholding Based Data Mining in Peer-to-Peer Systems. In Proceedings of SDM, pages 430--441, 2006. Google Scholar Cross Ref; R. Wolff, K. Bhaduri, and H. Kargupta. A generic local algorithm for mining data streams in large distributed systems. 21(4):465--478, April 2009. Google Scholar Digital

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(PDF) Collective Sequential Pattern Mining in Distributed

[14] R.Wolff, K. Bhaduri, and H. Kargupta: A Generic Local Algorithm for Mining Data St re ams in Large Distributed Systems. IEEE Transactions on Knowledge and Data Eng

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Lightweight Monitoring of Distributed Streams ACM

Ran Wolff, Kanishka Bhaduri, and Hillol Kargupta. 2009. A generic local algorithm for mining data streams in large distributed systems. TKDE 21, 4 (2009), 465--478. Google Scholar Digital Library; James Yeh. 2006. Real Analysis: Theory of Measure and Integration Second Edition. World Scientific Publishing Company. Google Scholar Cross Ref

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Distributed Systems Reading List GitHub Pages

A Distributed Systems Reading List Introduction I often argue that the toughest thing about distributed systems is changing the way you think. The below is a collection of material I've found useful for motivating these changes. Thought Provokers. Ramblings that make you think about the way you design. Not everything can be solved with big servers, databases and transactions. Harvest, Yield

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