Mining Very Large Databases With Parallel Processing
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Mining Very Large Databases with Parallel Processing
Author: Alex A. Freitas
language: en
Publisher: Springer Science & Business Media
Release Date: 1997-11-30
Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.
Mining Very Large Databases with Parallel Processing
Author: Alex A. Freitas
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.
Parallel and Distributed Processing
Author: Jose Rolim
language: en
Publisher: Springer Science & Business Media
Release Date: 2000-04-19
This volume contains the proceedings from the workshops held in conjunction with the IEEE International Parallel and Distributed Processing Symposium, IPDPS 2000, on 1-5 May 2000 in Cancun, Mexico. The workshopsprovidea forum for bringing together researchers,practiti- ers, and designers from various backgrounds to discuss the state of the art in parallelism.Theyfocusondi erentaspectsofparallelism,fromruntimesystems to formal methods, from optics to irregular problems, from biology to networks of personal computers, from embedded systems to programming environments; the following workshops are represented in this volume: { Workshop on Personal Computer Based Networks of Workstations { Workshop on Advances in Parallel and Distributed Computational Models { Workshop on Par. and Dist. Comp. in Image, Video, and Multimedia { Workshop on High-Level Parallel Prog. Models and Supportive Env. { Workshop on High Performance Data Mining { Workshop on Solving Irregularly Structured Problems in Parallel { Workshop on Java for Parallel and Distributed Computing { WorkshoponBiologicallyInspiredSolutionsto ParallelProcessingProblems { Workshop on Parallel and Distributed Real-Time Systems { Workshop on Embedded HPC Systems and Applications { Recon gurable Architectures Workshop { Workshop on Formal Methods for Parallel Programming { Workshop on Optics and Computer Science { Workshop on Run-Time Systems for Parallel Programming { Workshop on Fault-Tolerant Parallel and Distributed Systems All papers published in the workshops proceedings were selected by the p- gram committee on the basis of referee reports. Each paper was reviewed by independent referees who judged the papers for originality, quality, and cons- tency with the themes of the workshops.