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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
R. Wolff, K. Bhaduri, H. 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. 465478. April 2009. K. Bhaduri, H. Kargupta. A Scalable Local Algorithm for Distributed Multivariate Regression. Statistical Analysis and Data Mining ...
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. ... kmeans clustering in ...
show more info Publication Name A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Publication Location IEEE Transactions on Knowledge and Data Engineering (TKDE), 21(4): 465478
Sep 03, 2012· Generic local algorithm for mining data streams 1. 1 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, fact that the data is static or rapidly changing.
Local Algorithm (local results) Definition Local algorithm or local results are the aftermath of a user''s search on search engines, it is used by search engines in ranking businesses or listing businesses. Local algorithm presents businesses according to their relevance in the user''s query. Local algorithm or results also describe...
(2009) A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Transactions on Knowledge and Data Engineering 21 :4, 465478. (2009) Local Construction of NearOptimal Power Spanners for Wireless Ad Hoc Networks.
L2GClust is able to keep a good approximation of the global clustering, using less communication than a centralized alternative, supporting the recommendation to use local algorithms for distributed clustering of streaming data sources.
Ran Wolff, Kanishka Bhaduri, Hillol Kargupta: A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems. IEEE Trans. Knowl. Data Eng. 21(4): 465478 (2009) 2008; 21 : Kanishka Bhaduri, Ran Wolff, Chris Giannella, Hillol Kargupta: Distributed DecisionTree Induction in PeertoPeer Systems.
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Jan 03, 2013· Identifying clusters is an important aspect of analyzing large datasets. Clustering algorithms classically require access to the complete dataset. However, as huge amounts of data are increasingly originating from multiple, dispersed sources in distributed systems, alternative solutions are required. Furthermore, data and network dynamicity in a distributed setting demand adaptable .
Shaowei Wang, ... Aditya Budi, in The Art and Science of Analyzing Software Data, 2015. Iterative pattern mining with MapReduce. We illustrate how to instantiate our general algorithm with CLIPPER, an iterative pattern mining algorithm [2], to create the distributed version CLIPPER, similarly to many frequent pattern/ sequence mining algorithms, explores the search .
A Survey of Distributed Mining of Data Streams. Authors; Authors and affiliations ... M. Datar, and R. Motwani. Chain: Operator scheduling for memory minimization in data stream systems. In Proceedings of the ... C. Salisbury, and S. Tuecke. The data grid: Towards an architecture for the distributed management and analysis of large scientific ...
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems ... distributed processing kmeans clustering data stream mining large distributed systems generic local algorithm message routing information retrieval load sharing ... IEEE Transactions on Knowledge and Data Engineering. Rocznik. 2009.
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems Article (PDF Available) in IEEE Transactions on Knowledge and Data Engineering 21(4):465 478 · .
Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (, by monitoring the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as ...
a generic local algorithm for mining data streams in large distributed systems ieee 2009 jdm52 a pure nash equilibriumbased game theoretical method for data replication across multiple servers ieee 2009 jdm53 histogrambased global load balancing in structured peertopeer systems ieee 2009 jdm54 multiscale representations for fast pattern ...
BibTeX MISC{Wolff_1ageneric, author = {Ran Wolff and Kanishka Bhaduri and Hillol Kargupta and Senior Member}, title = {1A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems}, year = {}}
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems . By Ran Wolff, Kanishka Bhaduri and Hillol 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 functionality such as message ...
Identifying clusters is an important aspect of analyzing large datasets. Clustering algorithms classically require access to the complete dataset. However, as huge amounts of data are increasingly originating from multiple, dispersed sources in distributed systems, alternative solutions are required. Furthermore, data and network dynamicity in a distributed setting demand adaptable clustering ...
large distributed system generic local algorithm mining data stream global state large network dynamic scenario decision tree thorough experimental analysis wireless sensor information retrieval communication cost global data mining model step approach message routing efficient local algorithm data mining model kmeans clustering wide class ...
Parallel and distributed computing is a matter of paramount importance especially for mitigating scale and timeliness challenges. This special issue contains eight papers presenting recent advances on parallel and distributed computing for Big Data applications, focusing on .
Aug 22, 2008· 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. Much of the system''s functionality such as message routing, information retrieval and load sharing relies on ...
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