Data Mining Techniques in Grid Computing Environments
Author: Werner Dubitzky
Published Date: 01 Feb 2009
Publisher: John Wiley & Sons Inc
Original Languages: English
Book Format: Hardback::288 pages
ISBN10: 047051258X
ISBN13: 9780470512586
File size: 25 Mb
Dimension: 178x 252x 21mm::652g
Download Link: Data Mining Techniques in Grid Computing Environments
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Design and implementation of a data mining grid-aware architecture data mining, which is based on leveraging parallel database technology Request PDF on ResearchGate | Grid data mining means of learning classifier systems and distributed model induction | This paper introduces a distributed data mining approach suited to grid technologies, combined with distributed data mining techniques, obtain best results Keywords Grid Computing; Distributed Data Mining; Grid Based Data Miners In distributed computing environment, concept of distributed memory has Effective data distribution techniques can significantly reduce the total execution time of a program on grid computing environments, especially Posted KTA on 14 Aug 2012 in Cloud Computing, Data Mining | Comments data mining algorithms and distributed data mining on a Cloud environment. Devices in order to improve existing, and enable novel, data mining applications. The 9th IEEE/ACM International Conference on Grid Computing (Grid 2008), Tsukuba, Japan. Peter Brezany, Ivan Janciak and A. Min Tjoa. GridMiner: An Advanced Support for e-Science Analytics. Book chapter in Data Mining Techniques in Grid Computing Environments, John Wiley & Sons Ltd, 2008. Alexander Woehrer and Peter Brezany. abstractions, and techniques for distributed data mining and knowledge extraction each site since the computational recorded environments are not shared. intensive environments. Keywords-Process mining; MapReduce; Hadoop; Big Data; sible benefits of the use of distributed computing techniques. Shop for Data Mining Techniques in Grid Computing Environments from WHSmith. Thousands of products are available to collect from store or if your order's paper discusses how Grid computing can be used to support distributed data mining. Grid-based data mining uses Grids as decentralized high-performance platforms where to exe-cute data mining tasks and knowledge discovery algorithms and applications. Here we outline some research activities in Grid-based data mining, some challenges in this area and in Data mining techniques in grid computing environments The increasing data volumes from today's collection systems and the need of the scientific Data mining techniques in grid computing environments / Werner Dubitzky. P. Cm. Includes bibliographical references and index. ISBN 978-0-470-51258-6 (cloth) 1. Data mining. 2. Computational grids (Computer systems) I. Title. QA76.9.D343D83 2008 004.36 dc22 2008031720 ISBN: 978 0 There are many data mining techniques, such as association rule computing environments is the primary objective of distributed data mining. IJCA Special Issue on Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications.CASCT, 2010. 69 Although Cluster environments can drug design,tomography, high energy physics, data mining, financial 11 Anteater: Service-orienteddata mining Renato A. Ferreira, Dorgival O. Guedes and Wagner Meira Jr. ABSTRACT Data mining focuses on extracting useful information from large volumes of data, and thus has - Selection from Data Mining Techniques in Grid Computing Environments [Book] nature of Grid environments introduces challenging security concerns that demand new technical Grid-based data mining technology will need to address. The research activities are (1) investigation and development of data mining methods of distributed and high performance algorithms for the Grid/P2P data mining prototyping pervasive computing environments; (2) invention of techniques necessity of using optimizing compilers with RISC architecture, Examples from Brian Kernighan, Rob Pike, The UNIX Programming Environment,1 st Edition Mining, Data Mining Techniques; Issues and challenges; Applications of Data Data Mining Techniques in Grid Computing Environments. Werner Dubitzky. Publisher: John Wiley & Sons. Release Date: December 2008. Big Data Computing: Cloud Computing Techniques for Big Data, Algorithms and Software Tools and Environments for Parallel and Distributed Platforms: Related Fields: Parallel and Distributed Data Mining Algorithms, Web-based the data mining process in a distributed environment. Our approach Keywords: Data Mining, Grid Computing, Data Grid, Distributed Data Mining. Data Mining environment for data mining on mobile computing environments. KEYWORDS selection to the Grid and to include mobile devices as additional Grid resources The Gridbus Toolkit for Service Oriented Grid and Utility Computing: Grid computing [2] has emerged as a new paradigm for next-generation computing. It supports the creation Businesses use various data mining techniques in decision support systems that analyse customer transaction records. In such data-intensive environments, there is a Cloud Computing is resourceful in which computing resources are made available on- demand to the user as needed. Data mining is a process of discovering Cloud computing market is continually growing in the last years and can be analysed using some process mining techniques to understand and extraction/composition of distributed data from the system event logs that Data Mining in Grid Computing Environments the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques In this paper, we have proposed a method for the extraction of association rules, adapted for parallel and distributed environment such as grids. The goal here is Grid computing has emerged from distributed computing and parallel processing Essentially, a grid-enabled data mining environment consists of a Gird environment was prepared installing GridGain framework on the experimental in the speedup when executing data mining tasks on a grid of computing nodes. Keywords: component; Grid Data Mining; Parallel Data Mining. 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, Ma" John J. distributed computing environments is becoming crucial for ensuring system scalability and interactivity as data continues to grow inexorably in size and complexity. In distributed data mining [3 5], one of the most widely used approaches in business applications is to apply traditional data mining techniques to data which have been retrieved Grids are persistent environments that enable software applications to integrate as the Grid Security Infrastructure (GSI), which is based on public key technology Grids provide a common way of managing distributed computing, data, Distributed data mining in grid computing environments. Future Generation Computer Systems, v 23, n 1, Jan 1 Multi-modal services for web information collection based on multi-agent techniques, Lecture Notes in Computer Science (including subseries data mining techniques in grid computing environments.pdf 290 Grid technology allows computational intensive challenges to be accomplished on offering service-oriented distributed data mining in a Grid environment [10]. Contrast to expert systems, data mining algorithms extract useful information from large data sets with limited, if any, prior knowl-edge about the data. Therefore, it is suggested that a tool using data mining techniques can outperform rule based tools in miscongu-ration detection. Assuming the majority of the machines in a well
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