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Foundations of Multidimensional and Metric Data Structures / Edition 1
     

Foundations of Multidimensional and Metric Data Structures / Edition 1

5.0 2
by Hanan Samet
 

ISBN-10: 0123694469

ISBN-13: 9780123694461

Pub. Date: 08/01/2006

Publisher: Elsevier Science

The field of multidimensional data structures is large and growing very quickly. Here, for the first time, is a thorough treatment of multidimensional point data, object and image-based representations, intervals and small rectangles, and high-dimensional datasets. The book includes a thorough introduction; a comprehensive survey to spatial and multidimensional

Overview

The field of multidimensional data structures is large and growing very quickly. Here, for the first time, is a thorough treatment of multidimensional point data, object and image-based representations, intervals and small rectangles, and high-dimensional datasets. The book includes a thorough introduction; a comprehensive survey to spatial and multidimensional data structures and algorithms; and implementation details for the most useful data structures. Along with the hundreds of worked exercises and hundreds of illustrations, the result is an excellent and valuable reference tool for professionals in many areas, including computer graphics, databases, geographic information systems (GIS), game programming, image processing, pattern recognition, solid modeling, similarity retrieval, and VLSI design. Award Winner in 2006 “Best Book” competition in Professional and Scholarly Publishing from the Association of American Publishers.

Morgan Kaufmann would like to congratulate Hanan Samet on receiving the UCGIS 2009 Research Award!

Read the announcement here: http://www.ucgis.org/summer2009/researchaward.htm

• First comprehensive work on multidimensional data structures available, a thorough and authoritative treatment.
• An algorithmic rather than mathematical approach, with a liberal use of examples that allows the readers to easily see the possible implementation and use.
• Each section includes a large number of exercises and solutions to self-test and confirm the reader's understanding and suggest future directions.
• Written by a well-known authority in the area of spatial data structures who has made many significant contributions to the field.

The author's website includes: Spatial Index Demos

Product Details

ISBN-13:
9780123694461
Publisher:
Elsevier Science
Publication date:
08/01/2006
Series:
Morgan Kaufmann Series in Computer Graphics Series
Edition description:
New Edition
Pages:
1022
Product dimensions:
2.06(w) x 8.50(h) x 11.00(d)

Table of Contents

Multidimensional data is data that exists and changes in more than one dimension, by time, or spatially, or both, sometimes dynamically. Think here of tracking hurricane data in order to project the storm's path, for just one example. As spatial and other multidimensional data structures become increasingly important for the applications in game programming, data mining, bioinformatics, and many other areas—including astronomy, geographic information systems, physics, etc., the need for a comprehensive book on the subject is paramount. This book is truly a life's work by the author who is clearly the best person for the job.

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Foundations of Multidimensional and Metric Data Structures 5 out of 5 based on 0 ratings. 2 reviews.
Guest More than 1 year ago
The most complete book on the subject to date. In addition, to the huge amount of information covered, it also contains a thorough bibliography with over 2000 entries. The author uses an algorithmic approach with plenty of pseudo-code without resorting to complicated mathematical formulae. Clear explanations are given with more than 450 figures illustrating the ideas. The result is a wonderful place to explore spatial, multidimensional, and metric data structures on one's own or as part of a class. It has more than 1200 exercises that test the readers' understanding of the covered material, while many also develop the material in the text further. Solutions are provided to most of the exercises some of which test the readers' understanding of the covered materials, while many others develop the material in the text further as well as provide detailed pseudo code for many of the representations. The book is easily accessible to a wide range of readers who need not be programmers or computer scientists. Sample pages for the opening discussion in each of the book's four chapters are available at the publisher's web site. This book goes far beyond Hanan Samet's previous books containing completely new material such as a thorough discussion of image- and object-based representations, as well as an entire chapter on high-dimensional and metric data representations which together comprise almost two-thirds of the book. In addition, the new book expands considerably the discussion of point data in his out of print book titled 'The Design and Analysis of Spatial Data Structures,' which though still contains some material that is not in the new book. The new book has no overlap with his other out of print book titled 'Applications of Spatial Data Structures: Computer Graphics, Image Processing and GIS'. To summarize, this is another wonderful book from the most respected authority in the field. From novice to expert, everyone can learn something from this true masterpiece.
Guest More than 1 year ago
A stunning 1000 page encyclopedia of spatial algorithms presented in the Knuth tradition. The general coverage is similar to an older, now out of print and expensive: 'Design and Analysis of Spatial Data Structures'. In a surprise, the new book is not only the size of a telephone directory, but it has double the number of useful pages. 4 extensive chapters cover data structures and algorithms for: points, objects and images, intervals and small rectangles, and the same data types in higher +dimensions. Within each chapter, the algorithms and clearly presented and are accompanied by an extensive use of figures. The algorithms which run from the expected to the exotic are summarized by the table of contents at the publisher's web site. Unexpected algorithms are also covered including: nearest neighbor finding which is useful for clustering applications, image pyramids, and object pyramids or hierarchies such as R-trees. The book has a textbook flavor with exercises at the end of each section where specifics are left for the student however, solutions and pseudo-code for many of the exercises are in a 300+ page appendix maintaining the book as a useful reference. This book is comprehensive, inexpensive, and in my mind - a must have.