Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) by Eibe Frank
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Temporal database systems are systems that provide special support for storing, querying, and updating historical and/or future data. Current DBMSs provide essentially no temporal features at all, but this situation is likely to change soon for a variety of reasons; in fact, temporal databases are virtually certain to become important sooner rather than later, in the commercial world as well as in academia. This book provides an in-depth description of the foundations and principles on which those temporal DBMSs will be built. These foundations and principles are firmly rooted in the relational model of data; thus, they represent an evolutionary step, not a revolutionary one, and they will stand the test of time.
This book is arranged in three parts and a set of appendixes:
* Preliminaries: Provides a detailed review of the relational model, and an overview of the Tutorial D language.
* Laying the Foundations: Explains basic temporal data problems and introduces fundamental constructs and operators for addressing those problems.
* Building on the Foundations: Applies the material of the previous part to issues of temporal database design, temporal constraints, temporal query and update, and much more.
* Appendixes: Include annotated references and bibliography, implementation considerations, and other topics.
* Describes a truly relational approach to the temporal data problem.
* Addresses implementation as well as model issues.
* Covers recent research on new database design techniques, a new normal form, new relational operators, new update operators, a new approach to the problem of "granularity," support for "cyclic point types," and other matters.
* Includes review questions and exercises in every chapter.
* Suitable for both reference and tutorial purposes.
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