Mittra Database Performance Tuning and Optimization

Using Oracle
1. Auflage 2006
ISBN: 978-0-387-21808-3
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 513 Seiten

Examples abound in database applications of well-formulated queries running slowly, even if all levels of the database are properly tuned. It is essential to address each level separately by focusing first on underlying principles and root causes, and only then proposing both theoretical and practical solutions. "Database Performance Tuning and Optimization" comprehensively addresses each level separately by focusing first on underlying principles and root causes, and then proposes both theoretical and practical solutions using Oracle 8i examples as the RDBMS. The book combines theory with practical tools (in the form of Oracle and UNIX shell scripts) to address the tuning and optimization issues of DBAs and developers, irrespective of whether they use Oracle. Topics and features:

* An integrated approach to tuning by improving all three levels of a database (conceptual, internal, and external) for optimal performance
* Balances theory with practice, developing underlying principles and then applying them to other RDBMSs, not just Oracle
* Coverage of data warehouses provides readers much needed principles and tools for tuning large reporting databases
* Coverage of web-based databases
* Appendix B shows how to create an instance and its associated database and all its objects
* Provides useful exercises, references, and Oracle 8i and select 9i examples

Based on nearly two decades of experience as an Oracle developer and DBA, the author delivers comprehensive coverage of the fundamental principles and methodologies of tuning and optimizing database performance. Database professionals and practitioners with some experience developing, implementing, and maintaining relational databases will find the work an essential resource. It is also suitable for professional short courses and self-study purposes.

Written for: Practitioners, professionals

Keywords:
Databases
information systems

Autoren/Hrsg.

Mittra, Sitansu S.

Leseproben

2 Stochastic Shape Theory (p. 75)

Christian Cenker
Georg Pflug
Manfred Mayer

Stochastic models and statistical procedures are essential for pattern recognition. Linear discriminant analysis, parametric and nonparametric density estimation, maximumlikelihood classi.cation, supervised and nonsupervised learning, neural nets, parametric, nonparametric, and fuzzy clustering, principal component analysis, simulated annealing are only some of the well-known statistical techniques used for pattern recognition. Markov models and other stochastic models are often used to describe statistical characteristics of patterns in the pattern space.

We want to concentrate on modeling and feature extraction using new techniques.We do not model the characteristics of the pattern space but the generation of the patterns, i.e., modeling the pattern generation process via stochastic processes. Furthermore, wavelets and wavelet packets will help us to construct a feature extractor. Applying our models to a sample application we noticed the lack of global non-linear optimization algorithms. Thus, we added a section on optimization, in which we present a modification of a multi-level single-linkage technique that can be used in high-dimensional feature spaces.

2.1 Shape Analysis

A project on o.ine signature verfication shows the need for new approaches. Standard methods do not show the wanted accuracy, nevertheless, they have been implemented at a first stage in order to compare the results. As all signatures of one person are of di.erent but similar shape we look for a description of the similarity and the di.erence. First, a signature is a special form of curve, we discard all color, thickness and "pressure" information from the scanned signature (cf. (AYF86)), leaving only a thinned polygonal shape. We have a connected skeleton of the "contour". The .rst problem to solve is the parameterization of the curve, i.e., to get a onedimensional function that represents the two-dimensional signature, as our constraints are on the one hand to use as little data for storage of the signatures as possible and, on the other hand, to develop fast algorithms. Thus, using only one-dimensional objects (functions) seem to be a feasible solution. We choose a change-in-angle parameterization of the curve, which has the advantages of shift, rotation and scale invariance (cf. (Nie90)).