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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.
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.
This book constitutes the thoroughly refereed joint postproceedings of the Third International Workshop on Knowledge Discovery in Inductive Databases, KDID 2004, held in Pisa, Italy in September 2004 in association with ECML/PKDD. Inductive Databases support data mining and the knowledge discovery process in a natural way. In addition to usual data, an inductive database also contains inductive generalizations, like patterns and models extracted from the data. This book presents nine revised full papers selected from 23 submissions during two rounds of reviewing and improvement together with one invited paper. Various current topics in knowledge discovery and data mining in the framework of inductive databases are addressed.
This volume contains the proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA '97). DASFAA '97 focused on advanced database technologies and their applications. The 55 papers in this volume cover a wide range of areas in the field of database systems and applications ? including the rapidly emerging areas of the Internet, multimedia, and document database systems ? and should be of great interest to all database system researchers and developers, and practitioners.
Content Description #Includes bibliographical references and index.
Proceedings of the Third International Conference on Data and Knowledge Bases: Improving Usability and Responsiveness compiles papers presented at the Third International Conference on Data and Knowledge Bases held in Jerusalem, Israel on June 28-30, 1988. This book discusses the management system for graph-like documents, selection of processing strategies for different recursive queries, and supporting concurrent access to facts in logic programs. The design considerations for a Prolog database engine, experience with the domain algebra, and two level transaction management in a multiprocessor database machine are also described. This publication likewise covers the non-deterministic choice in Datalog and locally balanced compact Trie Hashing. This compilation is a good source for researchers and specialists of disciplines related to computer science.
This volume contains the proceedings of the eleventh British National Conference on Databases, held at Keele University, England. A dominant themein the volume is the provision of the means to enhance the capabilities of databases to handle information that has a rich semantic structure. A major research question is how to achieve such a semantic scale-up without sacrificing performance. There are currently two main paradigms within which it is possible to propose answers to this question, deduction-oriented and object-oriented. Both paradigms are well represented in this collection, with the balance in the direction of the deductive approach, which is followed by both the invited papers, by Michael Freeston from the European Computer-Industry Research Centre in Munich and Carlo Zaniolo from the University of California at Los Angeles. In addition, the volume contains 13 full papers selected from a total of36 submissions.