DSS News
                 D. J. Power, Editor
            April 9, 2006 -- Vol. 7, No. 8

    A Free Bi-Weekly Publication of DSSResources.COM 
          approximately 1,600 Subscribers 

       "Decision Support for Global Enterprises"


* Ask Dan! - Is parallel database technology needed for data-driven
* DSS Conferences
* DSS News Releases 


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Ask Dan!

by Dan Power
Editor, DSSResources.COM

Is parallel database technology needed for data-driven DSS?

Few managers have heard of parallel computer processing and parallel
databases. Nonetheless the desire of managers for more and better
historical data is increasing the need for such capabilities. The
call for papers for the ACM Eighth International Workshop on Data
Warehousing and OLAP states that "Data Warehouse (DW) and Online
Analytical Processing (OLAP) technologies are the core of current
Decision Support Systems. ... Research in data warehousing and OLAP
has produced important technologies for the design, management and
use of information systems for decision support." 

Norman and Thanisch with Bloor Research Group argue the future of
commercial databases "is bound up with the ability of databases to
exploit hardware platforms that provide multiple CPUs." They also
note "There is a tremendous amount of confusion in the market over
parallel database technology, among both customers and the vendors.
The root of this is a general lack of understanding of the technical
issues on both sides. Although the workings of a parallel database
are more complex than an ordinary database, understanding it requires
more of a change in mind set than an astronomically high IQ.
Unfortunately, few decision makers have much understanding of
parallel database, and consequently, it is open season for database
and hardware marketing people to confuse the market with technical
mumbo-jumbo, that they don't fully understand themselves." Parallel
database technology makes it possible to process very large databases
for data-driven decision support.

What is the history of parallel database technology?

According to James Gray, "During the 1970s there was great enthusiasm
for database machines -- special-purpose computers that would be much
faster than general-purpose systems running conventional databases.
The problem was that general purpose systems were improving at 50%
per year, so it was difficult for customized systems to compete with
them. By 1980, most researchers recognized the futility of
special-purpose approaches, and the database machine community
switched to research on using arrays of general purpose processors
and disks to process data in parallel. The University of Wisconsin
was home to the major proponents of this idea in the US. Funded by
the government and industry, they built a parallel database machine
called Gamma. That system produced ideas and a generation of students
who went on to staff all the database vendors. Today the parallel
systems from IBM, Tandem, Oracle, Informix, Sybase, and AT&T all have
a direct lineage from the Wisconsin research on parallel database
systems. The use of parallel database systems for data mining is the
fastest-growing component of the database server industry." 

Also, Gray notes "projects at UCLA gave rise to Teradata." Today NCR
Teradata ( is the premier vendor of parallel
database software. Most of my experience with parallel database
software occurred at NCR Teradata Partners conferences. The current
NCR Massively Parallel Processing (MPP) platform is designed to run
the Teradata Database software efficiently for data warehousing and
decision support. 

What is parallel database processing?

This question is challenging to answer for a broad audience. I'll try
to limit the buzz words and technical jargon. I'll also emphasize 2
nontechnical examples.

Let's start with a simple generalization. Parallel processing divides
a computing task into smaller tasks that can be processed
independently. Hence, the larger task is completed more quickly.
Parallel relational database systems store data that is spread across
many storage disks and accessed by many processing units.
states massively parallel processing "is the coordinated processing
of a program by multiple processors that work on different parts of
the program, with each processor using its own operating system and

A Teradata Warehouse technical overview includes the following
example of parallel processing: "Imagine that you were handed a
shuffled stack of playing cards and were not allowed to scan the
cards beforehand. Then you were asked a simple question, 'How many
aces are in the stack?' The only way to get the answer would be to
scan the entire deck of cards. Now imagine that the same cards were
distributed among four people, each receiving one-fourth of the
cards. The time required to answer this same query is now reduced by
four times. Each person would simply have to scan their cards, and
the four totals would be aggregated for the correct answer. In this
simple example, we can refer to these four people as parallelized
units of work. As you can see, more available parallelized units of
work will result in faster query processing. The larger the data
volume and the more complex the queries, the bigger the payoff from
using parallel processing. It’s also important to note that the
most efficient way to distribute the playing cards (or data) is to
distribute them evenly among the four people (or parallelized units
of work)."

Mahapatra and Mishra provide another example: "Your local grocery
store provides a good, real-life analogy to parallel processing. Your
grocer must collect money from customers for the groceries they
purchase. He could install just one checkout stand, with one cash
register, and force everyone to go through the same line. However,
the line would move slowly, people would get fidgety, and some would
go elsewhere to shop. To speed up the process, your grocer doubtless
uses several checkout stands, each with a cash register of its own.
This is parallel processing at work. Instead of checking out one
customer at a time, your grocer can now handle several at a time."

So imagine many relational databases linked together where each
database has the same data organization and individual questions are
simultaneously asked of all the databases and individual answers are
then summarized.

Is parallel database technology critical to the future success of
data-driven DSS?

YES. According to Todd Walter of NCR Teradata, three issues are
driving the increasing use of parallel processing in database
environments: the need for increased speed or performance for large
databases, the need for scalability and the need for high
availability. Finally, Mahapatra and Mishra (2000) conclude
"Intra-query parallelism is very beneficial in decision support
system (DSS) applications, which often have complex, long-running
queries. As DSS have become more widely used, database vendors have
been increasing their support for intra-query parallelism."

In general, parallel processing is necessary to provide timely
results from complex, decision support database queries needed by
managers in data intensive organizations.


Abdelguerfi, M. and K. Wong, Parallel Database Techniques, 
Wiley-IEEE Computer Society Press, July 1998.

Barney, B., "Introduction to Parallel Computing," Livermore

DeWitt, D. J. and J. Gray, "Parallel Database Systems:
The Future of High Performance Database Processing", 
Communications of the ACM, Vol. 36, No. 6, June 1992,

Gray, J. N., "Database Systems: A Textbook Case of Research Paying
Off," 1997, 

Mahapatra, T. and S. Mishra, "Oracle Parallel Processing," O'Reilly,

Norman, M. G. and P. Thanisch, "Parallel Database Technology: An
and Comparison of Scalable Systems," Bloor Research Group, URL

Teradata Warehouse Technical Overview: Teradata Pioneered Data
Warehousing, EB-3025, 
September 2005, URL .

Walter, T., "Scalability, Performance, Availability," Teradata
Magazine Online, 


 Purchase Dan Power's DSS FAQ book 
 83 frequently asked questions about computerized DSS 


 DSS Conferences 

 1. Crystal Ball User Conference, May 1-3, 2006 at the
Westin Tabor Center in Denver, Colorado. Check .

 2. ISCRAM2006, the Third International Conference on Information 
Systems for Crisis Response and Management, Newark, New Jersey, USA, 
at the New Jersey Institute of Technology from May 14-17, 2006.
Check .

 3. ICKEDS 2006, the Second International Conference on Knowledge
Engineering and Decision Support, Lisbon, Portugal, May 9-12, 2006.
Check .

 4. CIDMDS 2006, International Conference on Creativity and
Innovation in Decision Making and Decision Support 
sponsored by IFIP WG 8.3, June 28th - July 1st 2006, London,
UK. Check .

 5. DEXA 2006, 17th International Conference on Database 
and Expert Systems Applications, September 4-8, 
2006, Krakow, Poland. Check .

 6. ICDSS 2007, 9th International Conference on DSS, Jan. 2-4, 2007, 
Calcutta, India. Theme: Decision Support for Global Enterprises.
Check . Papers due May 10, 2006.


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DSS News Releases - March 25, 2006 to April 8, 2006
Read them at DSSResources.COM and search the DSS News Archive

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03/27/2006 2006 GeoTec event programming and registration now online.

03/26/2006 Data Warehousing and Knowledge Discovery (DaWaK 2006)
conference abstract deadline April 1, 2006.

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