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, eBook

Scope The book provides comprehensive coverage of database performance tuning and opti- zation using Oracle 8i as the RDBMS. The chapters contain both theoretical discussions dealing with principles and methodology as well as actual SQL scripts to implement the methodology. The book combines theory with practice so as to make it useful for DBAs and developers irrespective of whether they use Oracle 8i. Readers who do not use Oracle 8i can implement the principles via scripts of their own written for the particular RDBMS they use. I have tested each script for accuracy and have included the sample outputs generated from them. An operational database has three levels: conceptual, internal, and external. The c- ceptual level results from data modeling and logical database design. When it is imp- mented via an RDBMS such as Oracle, it is mapped onto the internal level. Database - jects of the conceptual level are associated with their physical counterparts in the internal level. An external level results from a query against the database and, as such, provides a window to the database. There are many external levels for a single conceptual level.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Mittra, Sitansu S.

Inhaltsverzeichnis

Methodology.- Database Application Development.- Performance Tuning Methodology.- Tuning the Conceptual Level of a Database.- Oracle Tools for Tuning and Optimization.- Internal Level of an Oracle Database.- Tuning of Disk Resident Data Structures.- Tuning of Memory-Resident Data Structures.- Oracle Utility for Tuning and Optimization.- Optimization of the External Level of a Database.- Query Tuning and Optimization Under Oracle 8i.- Special Features of Oracle 8i and a Glimpse into Oracle 9i.- Contemporary Issues.- Tuning the Data Warehouse at All Levels.- Web-Based Database Applications.

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)).