DSS News
                       by D. J. Power
             February 17, 2002 -- Vol. 3, No. 4
         A Bi-Weekly Publication of DSSResources.COM

 Check the Revised Spreadsheet DSS page at DSSResources.COM


* DSS Wisdom
* Ask Dan! - Is a Data Warehouse a DSS?
* Spreadsheet DSS Tip - Protect Spreadsheet DSS - NEW!
* What's New at DSSResources.COM
* DSS News Stories


This newsletter has more than 750 subscribers from 
50 countries. Please forward this newsletter to people interested in 
Decision Support Systems or suggest they visit DSSResources.COM.


DSS Wisdom

According to E. F. Codd,  "For any system that is claimed to be a 
relational database management system, that system must be able to 
manage data entirely through its relational capabilities." 

Codd specified the relational data model in 1970 and he identified 12 
rules to determine whether a DBMS is relational and to what extent it is 
relational. The above statement is known as rule zero. It is fundamental 
in evaluating relational database management systems.

Codd, E. F. (1985), "Is Your DBMS Really Relational?" and "Does Your 
DBMS Run By the Rules?", ComputerWorld, October 14 and October 21.


  Call for Papers: Decision Support Systems mini-track at
         AMCIS 2002-Dallas. Deadline 03/15/2002.
    Check URL 


Ask Dan!

Is a Data Warehouse a DSS? What is a star schema? How does a snowflake 
schema differ from a star schema?

No, a data warehouse is not a Decision Support System. A data warehouse 
is however usually the "driver" and dominant component for a Data-driven 
DSS. A data warehouse is an organized collection of large amounts of 
structured data. It is a database designed and intended to support 
decision making in organizations. It is batch updated and structured for 
rapid online queries and managerial summaries of its contents.  
According to Bill Inmon (1993), who is often called the "father" of data 
warehousing, "a data warehouse is a subject-oriented, integrated, 
time-variant, nonvolatile collection of data".  Ralph Kimball (1996), 
another data warehousing expert, says "A data warehouse is a copy of 
transaction data specifically structured for query and analysis". A data 
warehouse is often the component that stores data for a Data-driven DSS.

When a data warehouse is included as a component in a Data-driven DSS, a 
DSS analyst or data modeler needs to develop a schema or structure for 
the database and identify analytic software and end user presentation 
software to complete the DSS architecture and design.  The DSS 
components need to be linked in an architecture that provides 
appropriate performance and scalability.  In some Data-driven DSS 
designs, a second multidimensional database management system (MDBMS) 
will be included and populated by a data warehouse built using a 
relational database management system (RDBMS). The MDBMS will provide 
data for on-line analytical processing (OLAP). It is common to build a 
data warehouse using an RDBMS from Oracle or IBM and then use query and 
reporting and analytical software from a vendor like Brio or Business 
Objects as part of the overall Data-driven DSS design. What some vendors 
call "business intelligence software" often provides the analytics and 
user interface functionality for a Data-driven DSS built with a data 
warehouse component.

In a data warehouse built using a RDBMS the most common data model is 
called the star schema. A related model is called a snowflake schema.
A star schema is organized around a central fact table that is joined to 
some dimension tables using foreign key references. The fact table 
contains data like price, discount values, number of units sold, and 
dollar value of sales. The fact table usually has some summarized and 
aggregated data and it is usually very large in terms of both fields and 
records. The basic premise of a star schema is that information can be 
classified into two groups: facts and dimensions. Facts are the core 
data elements one is analyzing. For example, units of individual items 
sold are facts, while dimensions are attributes about the facts. The 
star schema has also been called a star-join schema, data cube, data 
list, grid file, and multidimensional schema. The name star schema comes 
from the pattern formed by the entities and relationships when they are 
represented as an entity-relationship diagram. Metaphorically, the 
results of a specific business activity are at the center of the star 
schema database and are surrounded by dimensional tables with data on 
the people, places, and things that come together to perform the 
business activity. These dimensional tables are the points of the star. 

How does a snowflake schema differ from a star schema? A snowflake 
schema is an expansion and extension of a star schema to additional 
secondary dimensional tables. In a star schema each dimension is 
typically stored in one table; the snowflake design principle expands a 
dimension and creates tables for each level of a dimensional hierarchy. 
For example, a Region dimension may contain the levels Street, City, 
State and Country. In a star schema, all these attributes would be 
stored in one table, in a snowflake schema one would expand the schema 
and a designer might add city and state secondary tables. 

Well as the above discussion indicates creating the data model for a 
Data-driven DSS is a complex task.  Whether DSS data is stored in a flat 
file, a hierarchical or multidimensional database or a relational 
database management systems, a large, well-organized database of 
business facts provides the functionality for a Data-driven DSS. A data 
warehouse is only part of such a system, but when it is used the data 
component is the "driver" for decision support.

Inmon, W. H. "What is a Data Warehouse", PRISM Newsletter, Center for 
the Application of Information Technology, Washington University in St. 
Louis, vol. 1, no. 1, 1993.

Kimball, R. The Data Warehouse Toolkit: Practical Techniques for 
Building Dimensional Data Warehouses, 1996.


Spreadsheet DSS Tip - Protect Spreadsheet DSS

Often Spreadsheet DSS are distributed to multiple users in a company. 
This practice can lead to problems and hence many developers want to 
take steps to reduce the chance that the DSS will be changed or 
corrupted. One answer is to protect the application. To protect a 
Spreadsheet DSS built using MS Excel choose Tools|Protection|Protect 
Sheet. When the Protect Sheet dialog box opens, type in a password. 
Re-enter the password when requested and then click OK. A developer 
needs to plan carefully what cells will and will not be protected.


What's New at DSSResources.COM

02/10/2002 Revised Spreadsheet DSS Resources page.

02/05/2002 Posted case by Insightful Staff, "Merck Deploys Web-Based 
Drug Discovery Visualization System", Insightful, Inc., 2002, URL 


DSS News Stories: February 1 to 14, 2002

02/14/2002 U.S. Manufacturing Leader Murray, Inc. drives improved 
financial processes with Cognos finance.

02/13/2002 Crystal Decisions delivers enterprise web reporting for 
Microsoft Visual Studio .NET.

02/12/2002 Xcel Energy standardizes on Brio Software to manage energy. 

02/12/2002 Corporate Radar introduces Winery IQ, designed for decision 
makers in the wine industry.

02/11/2002 Segway selects Appshop as its Oracle Application Service 

02/06/2002 Business Objects launches BusinessObjects Supply Chain 
Intelligence. Check release.

02/05/2002 SAS establishes Campus Innovation Grants Program for higher 

02/05/2002 Tilion introduces new enterprise software solutions for 
logistics and supply chain execution.

02/05/2002 Visual Mining, Inc., announced the release of NetCharts 4.0, 
it has expanded data visualization capabilities.

02/04/2002 Bank Rakyat chose MicroStrategy’s Business Intelligence 

02/04/2002 IBM selected Fair, Isaac Decision System to power its new 
global credit solution.

02/04/2002 NetIQ enters database management market with introduction of 
NetIQ SQL Management Suite.

02/04/2002 Mikohn Gaming uses business intelligence provided by 


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