Beschreibung Mining Very Large Databases with Parallel Processing (Advances in Database Systems (9), Band 9). 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 ~ 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 .
Mining Very Large Databases with Parallel Processing ~ 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.
Mining Very Large Databases with Parallel Processing ~ 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 .
Advances in Databases and Information Systems - 21st ~ This book constitutes the proceedings of the 21st East European Conference on Advances in Databases and Information Systems, ADBIS 2017, held in
CiNii 図書 - Mining very large databases with parallel ~ Mining very large databases with parallel processing. by Alex A. Freitas and Simon H. Lavington （The Kluwer international series on advances in database systems） Kluwer Academic Publishers, c1998. 大学図書館所蔵 件 / 全 15 件. 会津大学 情報センター (附属図書館) 図. QA76.9.D3. OPAC. 大阪市立大学 学術情報総合センター 工. 007.1//F46//9466 16100094669 .
Mining a large database with a parallel database server ~ Mining Very Large Databases with Parallel Processing, Kluwer Academic Publishers, Dordrecht (1998) Google Scholar. V Ganti, J Gehrke, R RamakrishnanMining very large databases. IEEE Computer, 32 (8) (1999), pp. 38-45. Google Scholar. R. Agrawal, K. Shim, Developing tightly coupled data mining applications on a relational database system, in: Proceedings of the Second International Conference .
Parallel Regular-Frequent Pattern Mining in Large Databases ~ Parallel Regular-Frequent Pattern Mining in Large Databases. G Vijay Kumar, Dr V Valli Kumari. Abstract—Mining interesting patterns in various domains is an important area in data mining and knowledge discovery process. A number of parallel and distributed frequent pattern mining algorithms have been proposed so far for the large and/or .
Big data mining with parallel computing: A comparison of ~ The parallel and cloud computing platforms are considered a better solution for big data mining. The concept of parallel computing is based on dividing a large problem into smaller ones and each of them is carried out by one single processor individually. In addition, these processes are performed concurrently in a distributed and parallel manner. There are two common methodologies used to .
Parallel Data Mining Using Multi-Core Computing ~ Team 63 Parallel Data Mining Using Multi-Core Computing 5 consequence of the huge size of real-world databases and data warehouses. Real-world database systems are large with respect to at least three dimensions. Besides the speed of search, the accuracy is also a problem that needs to be addressed. A good example would be Internet searching .
Sci-Hub ~ The Open Access is a new and advanced form of scientific communication, which is going to replace outdated subscription models. We stand against unfair gain that publishers collect by creating limits to knowledge distribution. Sci-Hub. support the project. The project is supported by user donations. Imagine the world with free access to knowledge for everyone ‐ a world without any .
Databases and Data Mining - dummies ~ Data collected by large organizations in the course of everyday business is usually stored in databases. But database administrators may not be willing to allow data miners direct access to these data sources, and direct access may not be the best option from your point of view either. Direct access to operational (used for routine business operations) databases can be a bad idea because .
Parallel data mining for very large relational databases ~ An interval classifier for database mining applications. Proc. 18th Int. Conf. Very Large Databases, 560–573. Vancouver, 1992. Google Scholar [2] M.P. Burwen. The White Cross parallel database servers. The Superperformance Computing Service. Product/Technology Review No. 145. (Available from 2685 Marine Way, Suite 1212, Mountain View, CA, USA, 94043.) Google Scholar [3] M. Holsheimer and A .
Parallel Database Systems / SpringerLink ~ Very large databases are typically accessed through high numbers of concurrent transactions (e.g., performing on-line orders on an electronic store) or complex queries (e.g., decision-support queries). The first kind of access is representative of On-Line Transaction Processing (OLTP) applications while the second is representative of On-Line Analytical Processing (OLAP) applications .
Transactions on Large-Scale Data- and Knowledge-Centered ~ Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX Special Issue on Database- and Expert-Systems Applications . Herausgeber: Hameurlain, A., Wagner, R., Benslimane, D., Damiani, E., Grosky, W. (Eds.) Vorschau. Contains revised and extended versions of the best seven papers presented at DEXA 2017; Covers a wide range of database- and expert-systems applications; Topics .
Data Mining Architecture - Data Mining Types and ~ In this architecture, data mining system does not use any functionality of a database. A no-coupling data mining system retrieves data from a particular data sources. The no-coupling data mining architecture does not take any advantages of a database. That is already very efficient in organizing, storing, accessing and retrieving data.
Integration of Data Mining and Relational Databases ~ database systems [7]. 2. Related Work in Data Mining Research In the last decade, significant research progress has been made towards streamlining data mining algorithms. There has been an explosion of work (e.g., [1]) in scaling many major data mining techniques to work with large data sets, i.e., ensuring that the algorithms are “disk-aware .
Integration of Data Mining and Relational Databases ~ Proceedings of the 26th International Conference on Very Large Databases / January 2000. Published by Very Large Data Bases Endowment Inc. Download BibTex. In this paper, we review the past work and discuss the future of integration of data mining and relational database systems. We also discuss support for integration in Microsoft SQL Server 2000. All articles published in this journal are .
Parallel Data Mining with DBMS Facilities / SpringerLink ~ The basic motivation is that most large data warehouses are already stored on parallel database servers, offering high-performance DBMS facilities (Chapter 8). Hence, it seems natural that these servers should be used to realize a cost-effective, holistic framework for parallel data mining and for the entire knowledge discovery process.
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CHAPTER-25 Mining Multimedia Databases - Data Mining and ~ To facilitate the multidimensional analysis of large multimedia databases, multimedia data cubes can be designed and constructed in a manner similar to that for traditional data cubes from relational data. A multimedia data cube can contain additional-dimensions and measures for multimedia information, such as color, texture, and shape. Let's examine a multimedia data mining system prototype .
(PDF) Data mining: An overview from a database perspective ~ The development of such systems has been originally motivated by the need to speed-up the process of analysis of huge amount of data stored in very large databases. The approach is applied in two .
Topic 5 Parallel and Distributed Databases, Data Mining ~ Intensive data consuming applications are running on very large databases (on data warehouses, on multimedia databases) with the task to extract information diamonds. Data mining is one of the key applications here. However, these intensive data consuming applications suffer from performance problems and single database sources. Introducing data distribution and parallel processing help to .
Advances in Knowledge Discovery and Data Mining ~ 1 From Data Mining to Knowledge Discovery: An Overview % Usama M. Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth 1 1 FOUNDATIONS 2 The Process of Knowledge Discovery in Databases: A Human-Centered Approach X Ronald J. Brachman and Tej Anand 37 3 Graphical Models for Discovering Knowledge Wray Buntine ' 59 ^ 4 A Statistical Perspective on Knowledge Discovery in Databases ^ John Elder IV .
Data Mining 2017 / Learning & Adaptive Systems Group ~ Data Mining: Learning from Large Data Sets Many scientific and commercial applications require us to obtain insights from massive, high-dimensional data sets. In this graduate-level course, students will learn to apply, analyze and evaluate principled, state-of-the-art techniques from statistics, algorithms and discrete and convex optimization for learning from such large data sets. VVZ .
Parallel and Distributed Data Mining: An Introduction ~ large-scale data mining application. This chapter presents a survey on large-scale parallel and distributed data mining algorithms and systems, serving as an introduction to the rest of this volume. It also discusses the issues and challenges that must be overcome for designing and im-plementing successful tools for large-scale data mining. 1 Introduction Data Mining and Knowledge Discovery in .