This manual describes implemention issues for an Oracle8 distributed database
system. It also introduces the tools and utilities available to assist you in implementing
and maintaining your distributed system.
Oracle8 Distributed Database Systems contains information that describes the features
and functionality of the Oracle8 and the Oracle8 Enterprise Edition products.
Oracle8 and Oracle8 Enterprise Edition have the same basic features. However, several
advanced features are available only with the Enterprise Edition, and some of
these are optional....
What constitutes a just tax system, and what are its moral foundations? Should a society's tax regime be designed to achieve a just distribution of wealth among its citizens, or should such a regime be designed to promote economic growth, rising standards of living, and increasing levels of employment? Are these two goals compatible or incompatible? Why should justice not require, or at least lead to, an increase in general prosperity? The essays in this volume examine the history of tax policies and the normative principles that have informed the selection of various types of taxes and tax...
Control the flow of information
security, accessibility, data control
save cost on local work stations and peripherals
Simplify data / software management
Backups, IS maintenance
Share information with multiple users
reduce local need CPU power
Master / Slave
(Similar to the teacher- student relationship)
Peer to Peer
(Similar to the workgroup concept)
Client / Server
(Similar to an automated teller transaction)
In Ebook Principles of Distributed Database Systems deﬁnes the fundamental concepts and set the framework for discussing distributed databases. We start by examining distributed systems in general in order to clarify the role of database technology within distributed data processing, and then move on to topics that are more directly related to DDBS.
–Function Blocks, Functions, Data Types, Programs
–Ladder Diagram (LD) for logic control (“power flow”)
–Function Block Diagram (FBD) for regulatory control (“data flow”)
–Sequential Function Chart (SFC) for state-machine control
–Structured Text (ST) for information processing
–Instruction List (IL) for assembly-level programming
•A Mature, Internationally Adopted Standard
–First edition: 1993
–Second edition: 2001...
Data Modeling Techniques for Data Warehousing
Chuck Ballard, Dirk Herreman, Don Schau, Rhonda Bell, Eunsaeng Kim, Ann Valencic
International Technical Support Organization http://www.redbooks.ibm.com
International Technical Support Organization
Data Modeling Techniques for Data Warehousing February 1998
.Take Note! Before using this information and the product it supports, be sure to read the general information in Appendix B, “Special Notices” on page 183.
his monograph presents methods for full comparative distributional analysis based on the relative distribution. This provides a general integrated framework for analysis, a graphical component that simplifies exploratory data analysis and display, a statistically valid basis for the development of hypothesis-driven summary measures, and the potential for decomposition - enabling the examination of complex hypotheses regarding the origins of distributional changes within and between groups.
The sample from a population applet lets the user select
samples of various sizes from a wide range of population
shapes including uniform, bell-shaped, skewed, and
binary populations (including a range of values for the
population proportion, p ). In addition, one can alter
any of the default populations to create a custom distribution
by dragging the mouse over the population
or by going to Custom binary and typing in the desired
population proportion. Small samples are drawn in an
animated fashion to help students understand the basic
idea of sampling.
William Stallings Data and Computer Communications
Chapter 19: Distributed Applications
.Abstract Syntax Notation One ASN.1
Used to define format of PDUs Representation of distributed information
Supervised sequence-labeling systems in natural language processing often suffer from data sparsity because they use word types as features in their prediction tasks. Consequently, they have difﬁculty estimating parameters for types which appear in the test set, but seldom (or never) appear in the training set. We demonstrate that distributional representations of word types, trained on unannotated text, can be used to improve performance on rare words. We incorporate aspects of these representations into the feature space of our sequence-labeling systems. ...
Research on the discovery of terms from corpora has focused on word sequences whose recurrent occurrence in a corpus is indicative of their terminological status, and has not addressed the issue of discovering terms when data is sparse. This becomes apparent in the case of noun compounding, which is extremely productive: more than half of the candidate compounds extracted from a corpus are attested only once. We show how evidence about established (i.e.
We propose a set of open-source software modules to perform structured Perceptron Training, Prediction and Evaluation within the Hadoop framework. Apache Hadoop is a freely available environment for running distributed applications on a computer cluster. The software is designed within the Map-Reduce paradigm. Thanks to distributed computing, the proposed software reduces substantially execution times while handling huge data-sets. The distributed Perceptron training algorithm preserves convergence properties, thus guaranties same accuracy performances as the serial Perceptron. ...
We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diatheses, extracted from a very large annotated corpus. Distributions of four grammatical features are sufficient to reduce error rate by 50% over chance. We conclude that corpus data is a usable repository of verb class information, and that corpus-driven extraction of grammatical features is a promising methodology for automatic lexical acquisition. ...
Challenges in Machine Learning and Data Mining presents about generative vs. discriminative learning, learning from non-vectorial data, Beyond classification and regression, Distributed data mining, Machine learning bottlenecks, Intelligible models, Combining learning methods.
IN the course of an analysis of several samples of technical Russian undertaken as part of a study in mechanical translation, a number of statistical data reflecting the structure of these samples were compiled. One of these, the distribution of word length, is presented here as Fig.