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BIS4435 Discussion Topic 3 Question on DW and Operation database
By Nawaz Khan
Can you apply data mining tools on operational database for business intelligence or it is
absolutely necessary to have a data warehouse in order to apply data mining tools for
business intelligence.
Discuss and give feedback to others.
BIS4435 Discussion Topic 3 Answers on DT3: DW and Operation database
BIS4435 Discussion Topic 3 Answer by Laith Gharib
The question is “Why do we need to use a separate database to perform data mining and
reporting?” Many companies include major task on-line data mining tools and knowledge
management that work directly on operational data, some of these tools begin to confuse
us between the databases and warehouses.
Because most people are familiar with commercial related database systems, it is to
understand what a data warehouse is by comparing these two kinds of systems.
The major take of on-line operational database systems is to perform online transactions
processing (OLTP) systems. They cover most of the day to day operations of an
organisation, such as purchasing, inventory, manufacturing, banking, payroll and
accounting. Data warehouse systems, on the other hand, serve users or knowledge
workers in the role of data analysis and decision making. Such systems can organise and
present data in various formats in-order to accommodate the diverse needs of the
different users. These systems are known as online analytical processing OLAP systems.
I feel I need to talk about the major distinguishing features between OLTP and OLAP
which I have summarised as follow:
Users and system orientation: an OLTP system is customer oriented and is used for
transaction and query processing by clerks, clients, and information technology
professionals. An OLPA system is market oriented and is used for data analysis by
knowledge workers, including managers, executives and analseist.
Data contests: an OLTP system manages current data that, typically, are too detailed to
be easily used for decision making. An OLAP system manages large amounts of historical
data, provides facilities for summarization and aggregation and stores and manages
information at different levels of granularity. These features make the data easier to use
in informed decision making.
Database design: an OLTP system usually adopts and entity relationship data model and
an application oriented database design. And OLAP system typically adopts either a star
or snowflake model and a subject oriented database design.
View: an OLTP system focuses mainly on the current data within an enterprise or
department, without referring to historical data or data in different organisation. In
contrast, an OLAP system often spans multiple versions of a database schema. Due to the
evolutionary process of an organisation, OLAP systems also deal with information that
originates from different organisations, integrating information from many data stores.
Because of the huge volume, OLAP data are stored on multiple storage media.
Access patterns: the access pattern of an OLTP system consists mainly of short, atomic
transactions. Such a system requires currency control and recovery mechanisms,
however, access to OLAP systems, are mostly read only operations.
Summary:
Because operational databases store huge amount of data, you may wonder “why not
perform online analytical processing directly on such databases instead of spending
additional time and resources to construct a separate data warehouse?” a major reason
for a separation is to help promote the high performance of both systems. An operational
database is designed and tuned from known tasks and work loads, such as indexing and
hashing using primary keys searching for particular records and optimizing “canned”
queries. On the other hand data warehouse queries are often complex. They involve the
computation of large groups of data at summarised levels and may require the use of
special data organisation, access, and implementation methods based on multi
dimensional views. Processing OLAP queries in operational databases would substantially
degrade the performance of operational tasks.
Moreover, an operational database supports the concurrent processing of multiple
transactions. Concurrency control and recovery mechanism, such as locking and logging,
are acquired to ensure the consistency and robustness of transaction. An OLAP query
often needs read only access of data records for summarisation and aggregation.
Concurrency control and recovery mechanisms, if applied for such OLAP operations, may
jeopardise the execution of concurrent transactions and thus substantially reduce the
throughputs of an OLTP system.
References:
Data Mining: concepts and techniques-second edition- Jiawei Han and Micheline Kamber.
Logical database design principles: John Garmany, Jeff Walker and Terry Clarck.
BIS4435 Discussion Topic 3 Answer by Faith Ime Abakada
Data Mining tools can be applied not only on data warehouses but also on operational
database .
The ‘but’ there is whether operational database or data warehouse allows the application
of data mining tools for Business intelligence.
The basic difference between operational databases and data warehouses is that
operational databases are designed to support transaction processing whereas data
warehousing systems are designed to support online analytical processing (OLAP) which
elaborates report generation unlike that of the operational database that has minimal
back-end reporting. As a result, data warehouses are designed and optimized using
methodologies that drastically differ from that of operational database systems.
My response to the question would be that in as much as data mining can be used on
both, when Business Intelligence is included data mining tools would be more efficient
when applied on data warehouses.
Data warehousing is a decision support database maintained separately from the
organization’s operational database.
Data mining tools are designed to search through detailed historical data to identify
hidden patterns that are not obvious to humans or query tools and in the process, reveal
intelligence that can be integrated into business processes to provide predictive
capabilities for improving strategic business decision making.
A lot of companies in many industries such as financial services, telecommunications,
retail, and market research, business intelligence is a critical resource for decisionmakers.
Data warehouse is essential for business Intelligence because it provides the company
management with historical data from both internal and external sources whereas a day
to day operational database excludes external data that can be used to weigh and predict
the performance and future performance of the company and also how to attain such
targets.
In summary I would like to quote a Gartner Group report which emphasis on the
necessity of mining tools for Business Intelligence. ‘‘Information Democracy will emerge
in forward-thinking enterprises, with Business Intelligence information and applications
available broadly to employees, consultants, customers, suppliers, and the public. The
key to thriving in a competitive marketplace is staying ahead of the competition. Making
sound business decisions based on accurate and current information takes more than
intuition. Data analysis, reporting, and query tools can help business users wade through
a sea of data to synthesize valuable information from it - today these tools collectively fall
into a category called Business Intelligence.’’
http://www.scribd.com/doc/18638941/Data-Warehousing-and-Data-Mining
http://www.rpi.edu/datawarehouse/dw-about-cmp.html
BIS4435 Discussion Topic 3 Answer by Afiff Bahalwan
First of all, before answering the question, let us see the difference between operational
database and data warehouse.
Operational database can be defined as ‘the database-of-record, consisting of systemspecific reference data and event data belonging to a transaction-update system’
(geekinterview.com, 2009). This kind of database is created in order to handle daily
transactions for a business. Therefore, operational database is continuously updated and
reflect the current detail of the last transactions.
While data warehouse, as described by Inmon (1993), is ‘a subject-oriented, integrated,
time-variant, and non-volatile collection of data in support of management’s decisionmaking process’. In other words, data warehouse can be defined as a repository of an
organisation’s electronically stored data. Data warehouses are designed to provide
efficient analytical reporting. Instead of handling daily transactions, data warehouse is
developed to store historical data, for instance the data of the company within the last 5
years.
‘In the data warehouse, operational databases are not accessed directly to perform
information processing’ (gantthead.com, 2009). In fact, the operational database is the
source of data for the data warehouse. The main difference is operational database is
designed for real time business operations, while data warehouse is designed for analysis
of business measures.
To sum up, the answer for the question is yes, we can apply data mining tools on both
operational database and data warehouse. However, data mining tools would work
efficiently when applied on data warehouse. Data mining is the process of extracting data
in order to find patterns and trends. For that reason, Connolly (2005) says that data
mining requires a single, separate, clean, integrated, and self-consistent source of data.
Extract, Transform, and Load (ETL) data from the operational database into the
warehouse, in which the data is cleaned and organised into a meaningful manner, are the
critical steps in constructing a data warehouse. It becomes clear that data inside the
warehouse must have been already cleaned and organised. As a result, many
organisations are using data mining tools on a data warehouse for business intelligence.
References:
Connolly, T. M. & Begg, C. E. (2005). Database Systems: A Practical Approach to Design,
Implementation, and Management. Pearson Education: Harlow.
http://www.geekinterview.com/kb/Operational-Database.html
http://www.gantthead.com/content/processes/9076.cfm
BIS4435 Discussion Topic 3 Answer by Althea Walters
Before answering the question let us examine the difference between operational
database and Data warehouse.
Operational database is a “database-of-record, consisting of system-specific reference
data and event data belonging to a transaction-update system.” (www.metro-designdev.com, 2009) In other word, operational database contains data that is essential to the
day-to-day operation of the business. This data consistently changes through updates
and shows the current value of the last details.
Data warehouse is consisting of “informational database used to store sharable data
sourced from an operational database-of-record.”( www.metro-design-dev.com,2009) In
other word it is a repository of historical data that is used to forecast trends, analyze
reports and supports management decision making- stores historical data, of the
organization.
Therefore the answer is yes we can apply data mining tools to both operational data and
data warehouse since operational databases are the source data for data warehouses.
However, with operational data base the information extracted will be contradictory since
the values of operational databases are constantly changing through daily updates. As
such, according to wikipedia data-mining (“which is the process of extracting hidden
patterns and trends from data”) are much more efficient for data warehouses, since the
larger pool of information is much more consistent, and can be better used to analyze
reports and support management decision. (www.en.wikipedia.com, 2009)
References
www.metro-design-dev.com
www.en.wikipedia.org/wiki/Data_mining
BIS4435 Discussion Topic 3 Answer by Priyadharshini Packrisamy
Data mining is the process of extracting valid, previously unknown comprehensive and
actionable information from large database and using it to make crucial business
Decision.
This may be as simple as a cursory glance at a spreadsheet, to check the numbers for
"sanity". Or it may involve graphical analysis with a full-blown OLAP tool. In short, Data
Mining can be applied anywhere in your business or organization where you are
interested in identifying and exploiting predictable outcomes[1].
Operational Database is the database-of-record, consisting of system-specific reference
data and event data belonging to a transaction-update system. The operational database
is the source of data for the data warehouse.So Data mining technique can be applied to
operational database also.
But when we analyze the data mining process it needs cleaned, self consistent, separate,
integrated data[2].
The results of data mining study are useful if there is some way to further investigate the
uncovered patterns. Data warehouse provide the capability to go back to data source. In
DW data from multiple sources to discover as many interrelationship as possible.
Many data mining tools currently operate outside of the warehouse, requiring extra steps
for extracting, importing, and analyzing the data. Furthermore, when new insights require
operational implementation, integration with the warehouse simplifies the application of
results from data mining.[3]
So the conclusion is it is possible to apply data mining in operational database but data
mining in data warehouse is more effective compared to operational database.
References
[1] http://www.albionresearch.com/data_mining/why.php
[2] Connolly, T. M. & Begg, C. E. (2005). Database Systems: A Practical Approach to
Design, Implementation, and Management. Pearson Education: Harlow.
[3]http://www.thearling.com/text/dmwhite/dmwhite.htm
BIS4435 Discussion Topic 3 Answer by Chintankumar Patel
Data mining is the process of extracting valid, previously unknown comprehensive and
actionable information from large database and using it to make crucial business
Decision.This may be as simple as a cursory glance at a spreadsheet, to check the
numbers for "sanity". Or it may involve graphical analysis with a full-blown OLAP tool. In
short, Data Mining can be applied anywhere in your business or organization where you
are interested in identifying and exploiting predictable outcomes[1].
Operational Database is the database-of-record, consisting of system-specific reference
data and event data belonging to a transaction-update system. The operational database
is the source of data for the data warehouse.So Data mining technique can be applied to
operational database also. But when we analyze the data mining process it needs
cleaned, self consistent, separate, integrated data[2].The results of data mining study are
useful if there is some way to further investigate the uncovered patterns. Data warehouse
provide the capability to go back to data source. In DW data from multiple sources to
discover as many interrelationship as possible.Many data mining tools currently operate
outside of the warehouse, requiring extra steps for extracting, importing, and analyzing
the data. Furthermore, when new insights require operational implementation, integration
with the warehouse simplifies the application of results from data mining.[3]So the
conclusion is it is possible to apply data mining in operational database but data mining in
data warehouse is more effective compared to operational database.
References
[1] http://www.albionresearch.com/data_mining/why.php
[2] Connolly, T. M. & Begg, C. E. (2005). Database Systems: A Practical Approach to
Design, Implementation, and Management. Pearson Education: Harlow.
BIS4435 Discussion Topic 3 Answer by Durga Devi Rajan
Basically data mining is a technique to bring out statistical result of an organization by
analysing the data.
Data mining technique can be applied to any database but that dbase should obey the
following rules
>data in the dbase must be cleaned and integrated one
>data in the dbase should be collected from multiple dbase
> Dbase supposed to allow further investigation
ODS is a operational database which obey above mentioned condition and when we think
about data warehouse obviously supports all the above rules
Since historical data is one of the important concept to consider .With historical data we
can end up with better decision making. So it’s always best to apply data mining
technique to data warehouse then operational data base.
ref Database Systems A Practical approach to design by
connolly &begg
BIS4435 Discussion Topic 3 Answer by Sanjay Kumar
In my opinion we can apply data mining tools on operational database for business
intelligence but at the time when we are going to apply data mining tools on operational
management for business intelligence , should be aware of some facts like:- what is the
difference between data mining & data warehousing?
Data warehousing is the process of combining data from different data sources into one
database. Data mining is analyzing data to find relationships, some of which are nonintuitive. Data mining is often performed on data warehouses.
Data warehouse is a repository of an organization's electronically stored data. Data
warehouses are designed to facilitate reporting and analysis while Data mining is the
process of extracting patterns from data. data mining can be used to uncover patterns in
data samples, it is important to be aware that the use of non-representative samples of
data may produce results that are not indicative of the domain.
In Data Warehousing, the Operational Database is one which is accessed by an
Operational System to carry out regular operations of an organization.Data Warehouses
use an OLAP Database (Online Analytical Processing) which is optimized for faster
queries.
REFERENCES:http://en.wikipedia.org/wiki/Data_mining
http://en.wikipedia.org/wiki/Data_warehouse
http://en.wikipedia.org/wiki/Operational_database
BIS4435 Discussion Topic 3 Answer by Allen Rajiev
We can apply data mining tools on operational database for business intelligence. Though
we can do this, it is not the best way to use Data mining tools.
Operational databases are designed for storing and retrieving day-to-day transactions
happening in the organization which will be helpful for transaction processing systems in
the organization. Data mining tools are having enormous capabilities to do data
preparation, selection of data mining operations, facilities for understanding results,
improving performance and so on. If we are using such a tool on an operational database
the tool cannot be utilized to its full capacity and also it will affect the performance of the
operational database.
Good BI can be gathered only by analyzing historical data which are usually part of Data
Warehouse. It is better to have a separate source of data for Data mining tools as it can
work on it for data cleansing, data describing, data transforming and data sampling. Data
mining tools require only read only access to the data. An operational DB is usually used
for read/write operations.
If data mining tools are applied on operational database it will surely affect the
performance of DB and hence it’s advisable to use data mining tools on Data warehouse.
Reference :
1. Thomas M Connolly & Carolyn E Begg , Database Systems : A Practical approach to
design, Implementation and management Fourth Edition Pages(1241)
2. Jiawei Han & Micheline Kamber, Data Mining concepts and Techniques, Second Edition
Pages(109,110)
BIS4435 Discussion Topic 3 Answer by Maria Chrysandreou
BIS4435 Discussion Topic 3 Answer by Sujatha Dingari
Data mining software allows users to analyze large databases to solve business decision
problems. Data mining is, in some ways, an extension of statistics, with a few artificial
intelligence and machine learning twists thrown in. Like statistics, data mining is not a
business solution, it is just a technology.
Data mining is not an “intelligence” tool or framework. Business intelligence, typically
drawn from an enterprise data warehouse, is used to analyze and uncover information
about past performance on an aggregate level. Data warehousing and business
intelligence provide a method for users to anticipate future trends from analyzing past
patterns in organizational data. Data mining is more intuitive, allowing for increased
insight beyond data warehousing. An implementation of data mining in an organization
will serve as a guide to uncovering inherent trends and tendencies in historical
information. It will also allow for statistical predictions, groupings and classifications of
data.
DATA WAREHOUSING:
Data warehouse is a repository of an organization's electronically stored data. Data
warehouses are designed to facilitate reporting and analysis.
This classic definition of the data warehouse focuses on data storage. However, the
means to retrieve and analyze data, to extract, transform and load data, and to manage
the data dictionary are also considered essential components of a data warehousing
system. Many references to data warehousing use this broader context. Thus, an
expanded definition for data warehousing includes business intelligence tools, tools to
extract, transform, and load data into the repository, and tools to manage and retrieve
metadata.
Data warehouse is essential for business Intelligence because it provides the company
management with historical data from both internal and external sources whereas a day
to day operational database excludes external data that can be used to weigh and predict
the performance and future performance of the company and also how to attain such
targets.
http://www.thearling.com/
http://www.aspfree.com/c/a/MS-SQL-Server/Using-Data-Mining-for-BusinessIntelligence/
BIS4435 Discussion Topic 3 Answer by Mohamed Sameem Syed Sahabudeen
Data Mining is “A process of transforming data into information and making it available to
users in a timely enough manner to make a difference”(Forrester Research, April 1996
1. Data quality: Different sources typically use inconsistent data representations, codes,
and formats which have to be reconciled.
To this end, we can say that Data mining automates analysis of operational data with the
intention of finding previously unknown data characteristics, relationships, dependencies,
and/or trends, so therefore, data mining, can be used for business intelligence for both
operational database, and data warehouse.
Chapter 8 of Database Modeling and Design: Logical Design, Fourth Edition by Toby
Teorey, published by Elsevier in 2006.
In the data warehouse, operational databases are not accessed directly to perform
information processing’ (gantthead.com, 2009). In fact, the operational database is the
source of data for the data warehouse. The main difference is operational database is
designed for real time business operations, while data warehouse is designed for analysis
of business measures. think data mining tools can be apllied on operational database for
business intelligence and it is not necessary to have a datawarehouse.But data mining is
a long process and is time consuming. And where as,datawarehouse focuses on data
storage which is usually used for analysis,storage purpose,etc.So data warehouse helps
make the retriving of information easier and helps to get a better and faster result
Data warehouse is essential for business Intelligence because it provides the company
management with historical data from both internal and external sources whereas a day
to day operational database excludes external data that can be used to weigh and predict
the performance and future performance of the company and also how to attain such
targets.
Data warehouse is a repository of an organization's electronically stored data. Data
warehouses are designed to facilitate reporting and analysis while Data mining is the
process of extracting patterns from data. data mining can be used to uncover patterns in
data samples, it is important to be aware that the use of non-representative samples of
data may produce results that are not indicative of the domain.
In Data Warehousing, the Operational Database is one which is accessed by an
Operational System to carry out regular operations of an organization.Data Warehouses
use an OLAP Database (Online Analytical Processing) which is optimized for faster
queries.
http://en.wikipedia.org/wiki/Data_warehouse
http://www.aspfree.com/c/a/MS-SQL-Server/Using-Data-Mining-for-BusinessIntelligence/
BIS4435 DISCUSSION TOPIC 3 READING MATERIAL