The Morgan Kaufmann Series in Data Management Systems
8 total works
Database Modeling and Design
by Toby J. Teorey, Sam Lightstone, and Tom Nadeau
Published 1 January 1994
Database Modeling & Design is a comprehensive guide to DB design for commercial database products and their applications. Throughout, Toby Teorey offers practical and immediately usable techniques for transforming the ER model into SQL data structures. New to this Edition: A simple introduction to the terms and concepts of relational databases for those new to DB application programming; a complete description of SQL with 20 sample queries; object data modeling and its relationship to ER modeling for easy conversion when needed; figures and examples utilize standard Chen notation; new coverage of DB tuning; and a new case study: an intuitive employee database example that allows the reader to concentrate on concepts rather than on the specifics of the database. Also includes solutions to selected exercises for those who want a taste of practical experience in DB design.
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references.
The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.
This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.
The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.
This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.
Data Mining: Know It All
by Soumen Chakrabarti, Earl Cox, Eibe Frank, Ralf Hartmut Guting, Jiawei Han, Xia Jiang, Micheline Kamber, Sam S. Lightstone, Thomas P Nadeau, and Richard E Neapolitan
Published 1 October 1999
This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.
Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues-including Web crawling and indexing-Chakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's work-painstaking, critical, and forward-looking-readers will gain the theoretical and practical understanding they need to contribute to the Web mining effort.
Database Modeling and Design: Logical Design
by Toby J. Teorey, Sam Lightstone, Tom Nadeau, and H V Jagadish
Published 1 January 2010
Physical Database Design
by Sam S. Lightstone, Toby J. Teorey, and Tom Nadeau
Published 21 March 2007
The rapidly increasing volume of information contained in relational databases places a strain on databases, performance, and maintainability: DBAs are under greater pressure than ever to optimize database structure for system performance and administration.
Physical Database Design discusses the concept of how physical structures of databases affect performance, including specific examples, guidelines, and best and worst practices for a variety of DBMSs and configurations. Something as simple as improving the table index design has a profound impact on performance. Every form of relational database, such as Online Transaction Processing (OLTP), Enterprise Resource Management (ERP), Data Mining (DM), or Management Resource Planning (MRP), can be improved using the methods provided in the book.
Physical Database Design discusses the concept of how physical structures of databases affect performance, including specific examples, guidelines, and best and worst practices for a variety of DBMSs and configurations. Something as simple as improving the table index design has a profound impact on performance. Every form of relational database, such as Online Transaction Processing (OLTP), Enterprise Resource Management (ERP), Data Mining (DM), or Management Resource Planning (MRP), can be improved using the methods provided in the book.
Moving Objects Databases is the first uniform treatment of moving objects databases, the technology that supports GPS and RFID. It focuses on the modeling and design of data from moving objects - such as people, animals, vehicles, hurricanes, forest fires, oil spills, armies, or other objects - as well as the storage, retrieval, and querying of that very voluminous data.
It includes homework assignments at the end of each chapter, exercises throughout the text that students can complete as they read, and a solutions manual in the back of the book.
This book is intended for graduate or advanced undergraduate students. It is also recommended for computer scientists and database systems engineers and programmers in government, industry and academia; professionals from other disciplines, e.g., geography, geology, soil science, hydrology, urban and regional planning, mobile computing, bioterrorism and homeland security, etc.
It includes homework assignments at the end of each chapter, exercises throughout the text that students can complete as they read, and a solutions manual in the back of the book.
This book is intended for graduate or advanced undergraduate students. It is also recommended for computer scientists and database systems engineers and programmers in government, industry and academia; professionals from other disciplines, e.g., geography, geology, soil science, hydrology, urban and regional planning, mobile computing, bioterrorism and homeland security, etc.
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
by Earl Cox
Published 1 January 2005
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you'll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.
You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.
You don't need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.