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Author: Alex A. Freitas
Publisher: Springer Science & Business Media
ISBN: 0792380487
Category : Computers
Languages : en
Pages : 226
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Book Description
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.
Author: Alex A. Freitas
Publisher: Springer Science & Business Media
ISBN: 0792380487
Category : Computers
Languages : en
Pages : 226
Get Book Here
Book Description
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.
Author: Alex A. Freitas
Publisher: Springer Science & Business Media
ISBN: 1461555213
Category : Computers
Languages : en
Pages : 211
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Book Description
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.
Author:
Publisher:
ISBN:
Category : Database management
Languages : en
Pages : 496
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Book Description
Author: Petrus Maria Gerardus Apers
Publisher: Morgan Kaufmann
ISBN: 9781558601017
Category : Data base management
Languages : en
Pages : 488
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Book Description
Author: Preston Zhang
Publisher: CRC Press
ISBN: 042974952X
Category : Computers
Languages : en
Pages : 262
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Book Description
It is a major challenge to migrate very large databases from one system, say for example, to transfer critical data from Oracle to SQL Server. One has to consider several issues such as loss of data being transferred, the security of the data, the cost and effort, technical aspects of the software involved, etc. There a very few books that provide practical tools and the methodology to migrate data from one vendor to another. This book introduces the concepts in database migration with large sample databases. It provides step by step guides and screenshots for database migration tools. Many examples are shown for migrating Oracle, SQL Server and MySQL databases.
Author: Rakesh Agrawal
Publisher: Morgan Kaufmann
ISBN:
Category : Computers
Languages : en
Pages : 740
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Book Description
Author: Zongmin Ma
Publisher: IGI Global
ISBN: 1599041200
Category : Computers
Languages : en
Pages : 335
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Book Description
"This book integrates data management in databases with intelligent data processing and analysis in artificial intelligence. It challenges today's database technology and promotes its evolution"--Provided by publisher.
Author: Nikos Mamoulis
Publisher: Springer Science & Business Media
ISBN: 3642029817
Category : Business & Economics
Languages : en
Pages : 478
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Book Description
This volume constitutes the refereed proceedings of the 11th International Symposium on Spatial and Temporal Databases, SSTD 2009, held in Aalborg, Denmark, in July 2009. The 20 revised full papers presented together with 3 keynotes, 7 short papers, and 10 demonstration papers, were thoroughly reviewed and selected from a total of 62 research submissions and 11 demonstration submissions. The papers are organized in topical sections on spatial and flow networks, integrity and security, uncertain data and new technologies, indexing and monitoring moving objects, advanced queries, as well as on models and languages.
Author: Gavin Powell
Publisher: John Wiley & Sons
ISBN: 0764574906
Category : Computers
Languages : en
Pages : 496
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Book Description
From the #1 source for computing information, trusted by more than six million readers worldwide.
Author: Shinji Kikuchi
Publisher: Springer
ISBN: 3642120385
Category : Computers
Languages : en
Pages : 346
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Book Description
This book constitutes the refereed proceedings of the 6th International Workshop on Databases in Networked Information Systems, DNIS 2010, held in Aizu-Wakamatsu, Japan in October 2010. The 13 revised full papers presented together with 9 invited talks and 1 keynote lecture were carefully reviewed and selected for inclusion in the book. The workshop generally puts the main focus on data semantics and infrastructure for information management and interchange. The papers are organized in topical sections on networked information systems: infrastructure, access to information resources, information and knowledge management systems, information extraction from data resources, and geo-spatial decision making.
