COMBINING THE DATA WAREHOUSE AND
OPERATIONAL DATA STORE
Ahmed Sharaf Eldin Ahmed, Yasser Ali Alhabibi
Faculty of Computer and Information Sciences, Helwan University, Cairo, Egypt
Abdel Badeeh M. Salem
Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
Keywords: Data Warehousing (DW), Operational Data Stores (ODS).
Abstract: Many of small business organizations tend to combine the operational data stores (ODS) and data
warehousing (DW) in one structure in order to save the expenses of building two separate structures for
each of them. The purpose of this paper is investigating the expected obstacles that may affect organizations
that try to combine the ODS and DW in one structure. Both the analytical and comparative analysis are
used to investigate the obstacles and drawbacks that have been faced in combining the ODS and DW in one
structure.
1 INTRODUCTION
The classical structure for delivering business
intelligence contains two levels of data that are of
interest the data warehouse data (DW) and the data
mart data (Mattison, 1996). The data warehouse,
often called the current level of detail, contains the
bulk of the detailed data that has been collected and
integrated from the applications environment. The
data mart is a departmental subset of the current
detailed data that is shaped to meet the decision
support system (DSS) processing needs of that
particular department. For all the benefits of a data
warehouse and its associated data marts, there is still
a need for collective, integrated operational,
DSS/informational processing. When this need
arises, an operational data store (ODS) is in order.
An ODS is a hybrid structure that has equally strong
elements of operational processing and DSS
processing. This dual nature of the ODS easily
makes it the most complex architecture structure in
the corporate information factory (Harry, 1999).
Trying to combine the data warehouse (DW) (Singh,
H., 1999) and the operational data store (ODS)
(Dhor Vasant and Stein Roger, 1996) into the same
structure gets appreciation from many of small
business organizations (Inmon et al., 1998)
(specially financial managers and owners) for saving
expenses point of view, although it may be dispute
with the specifications of the corporate information
factory (CIF) (Farzad Shafiei, David Sundaram,
2004). It is theoretically possible to build such a
structure, and under very limited circumstances,
such a combination structure can be made to work
(Bolloju, 2001). Where there is a very small amount
of data and processing, and an abundance of
processing power, it is possible to merge an ODS
and DW into the same structure (Delic et al., 2001).
The capacity levels for the processor under normal
hours of utilization in order for the merger to
function properly. Unfortunately, it is not
economically feasible to purchase a powerful and
versatile machine and use it so sparsely. The
remainder of this paper is organized as follows. The
next section presents an overview about MTI
multidimensional database, DW, and ODS, the third
section demonstrates the experiments that are
applied to investigate the expected obstacles when
combining ODS and DW as incompatible
transaction types, in the first part of this section, the
principle and workload extraction are introduced,
followed by discussing the experiments' results. The
last section summarizes the conclusion and future
work.
282
Sharaf Eldin Ahmed A., Ali Alhabibi Y. and Badeeh M. Salem A. (2006).
COMBINING THE DATA WAREHOUSE AND OPERATIONAL DATA STORE.
In Proceedings of the Eighth International Conference on Enterprise Information Systems - DISI, pages 282-288
DOI: 10.5220/0002490802820288
Copyright
c
 SciTePress

2 MTI DATABASE, DW, AND
ODS
MTI company is one of Seoudi Group (Seoudi
Group, 1998) (SG) companies that is dealing in
vehicle business (marketing, sales, distribution,
customer support, and after sales services). MTI
drives its business for all vehicle brands in Seoudi
Group (NISSAN, FIAT, SUZUKI, DATCIA, and
BLAC) and in all categories of sales types (retail,
fleet, government, and wholesales). MTI has two
outlets for sales, client/server distributed system (7
client machines Pentium III-500 running under
windows 98, 2000, and XP platforms with 10 GB
HDD capacity, and 1 server machine Pentium III-
500 running under windows NT platform with 20
GB HDD capacity), Ethernet LAN network. MTI
has strong built in multi databases system (about 80
MB) for marketing, sales, and customer support
(relational multidimensional database system
running under MS access software and windows XP
platform). MTI has detailed historical data for the
period from Jan-1999 till Apr-2004. Metadata
(Nestorov and Jukic, 2003) and data marts
technologies are available for all varieties of
departmental and activities of business that covers
all analytical analysis for marketing, sales, and
customer support purposes. Figure 1 shows MTI
multidimensional databases model.
3 EXPERIMENTS
The following four experiments are run to
investigate the expected obstacles when combining
ODS and DW (Nestorov and Jukic, 2003) as
incompatible transaction types:
a. Combination of incompatible transaction types
(ODS and DW) in one machine (server) such as
current month sales figures and historical sales
figures for the previous month and the same month
last year as shown in figures 2 and 3.
Figure 2: Current data is mixed with historical data when the ODS is mixed with the DW.
ODS
data
DW
This month data
Year to date data Previous month data
Same month data last year
Figure 1: MTI Multidimensional Databases Model.
Product
Time
Customer
Product
Type
Segment
Class
Model
Description
Brand
Sub-Brand
Cit
y
Directora
State
Region
Time
Month
Q
uarte
r
Half-Yea
r
Yea
r
An
y
-
p
eriod
Customer
Distribution
Channel
Distribution
Channel
COMBINING THE DATA WAREHOUSE AND OPERATIONAL DATA STORE
283

b. Separation of incompatible transaction types
(ODS and DW) in two machines (ODS
transactions in server machine such as On Line
Transaction Processing – OLTP for the current
month sales figures and DW transaction such as
analytical transactions for historical sales figures
for the previous month and the same month last
year in administrator client machine) as shown in
figures 3 and 4.
c. Forced combination of incompatible types (ODS
and DW), firstly in large processor-mini computer-
S400 machine that belong to a sister company in SG
(Seoudi Group, 1998) called MM company, and
secondly, in small processor-server machine
Pentium III-500 as shown in figure 3.
d. Using a small machine-server to house a mixed
workload of ODS and DW as shown in figure 3.
3.1 Workload Extraction
The queries from the transaction log constitute a
workload that is treated by an SQL query analyzer.
The SQL query analyzer extracts all the attributes
that may be indexed (indexable attributes). It is
assumed that a workload similar to the one
presented in figure 5. Such a workload can be easily
TOTAL VEHICLE SALES (UP TO MRCH MONTH) BY SEGMENT
2004 2004 2003
Mar. Feb. MOVE Mar. Mar. YEAR TO DATE
MONTH MONTH % MONTH MONTH
VAR
% 2004 2003 VAR %
PASS 280 235 19.2 % 280 275 1.8 % 695 670 3.7 %
4WD 20 15 33.3 % 20 25 (20) % 55 50 10.0 %
SUB TOTAL
300 250 20.0 % 300 300 0.0 % 750 720 4.2 %
TRUCKS 175 125 40.0 % 175 135 29.6 % 500 475 5.3 %
BUSES 25 25 00.0 % 25 15 66.7 % 100 85 17.7 %
TOTAL SALES 500 400 25.0 % 500 450 11.1 % 1350 1280 5.5 %
TOTAL VEHICLE SALES (UP TO MRCH MONTH) BY BRAND
2004 2004 2003
Mar. Feb. MOVE Mar. Mar. YEAR TO DATE
MONTH MONTH % MONTH MONTH
VAR
% 2004 2003 VAR %
ALFA 7 4 75.0 % 7 6 16.7 % 18 16 12.5 %
DACIA 30 25 20.0 % 30 40 (25) % 110 125 (12) %
FIAT 93 85 9.4 % 93 103 (10) % 260 275 (4.5) %
NISSAN 300 221 35.8 % 300 236 27.1 % 832 744 11.9 %
SUZUKI 70 65 7.7 % 70 65 7.6 % 130 120 8.3 %
TOTAL SALES 500 400 25.0 % 500 450 11.1 % 1350 1280 5.5 %
Figure 3: Samples of Integrating Combined/Separated ODS and DW Information for DSS Purposes.
DW DW
ODS
ODS
ODS DW ODS DW
DW
DW
Figure 4: Mixing Combined Workloads across the Communication Lines.
The transmissions when the ODS and the
DW are se
p
arated.
The transmissions when the ODS and the DW
are combined.
ICEIS 2006 - DATABASES AND INFORMATION SYSTEMS INTEGRATION
284

obtained from the DBMS transaction log. To reduce
response time when running a database query, it is
better to build indexes on attributes that are used to
process query. These attributes belong to the
WHERE, ORDER BY, GROUP BY, and HAVING
clauses of SQL queries. In order to validate this
approach, it has been applied on a test ODS and a
test DW to measure the variance in response time in
the following four experimental situations:
combination of ODS and DW in one machine
(server); separation of ODS and DW in two
machines; forced combination of ODS and DW (in
smaller and larger processors as explained above);
and finally, using a small machine-server to house a
mixed workload of ODS and DW. The Test Protocol
Close-Result (TPC-R) decision-support benchmark
(Poess et al., 2002) (Transaction Processing
Council, 1999) is applied for these experiments on a
relational database. The TPC-R 1 GB database has
been generated and used the benchmark's 22 queries
(labeled Q1 to Q22). Taking into account that there
is no standard benchmark for DWs yet (TPC-DS is
still in development (Poess et al., 2002)). Hence, the
work is done on a small data mart. This data mart is
composed of an AbcVehInfo fact table and four
dimension tables: product, place, customer, and
date. It occupies 4 MB on disk. Starting from
previous analysis on this datamart, a realistic DSS
workload is also designed that is specifically
adapted to it. This workload includes both selection
and update operations. It cannot be presented in
detail here due to limitation of space. Both the TPC-
R database and the AbcVehInfo data mart have been
implemented within the Access DBMS. The test
protocol that was adopted is presented in figure 6.
This algorithm has been executed for various values
of the minsup (minimal support) parameters as
follows: the number of attributes varies from 1 to 7
and the number of indexes varies from 0 to 3. In
practice this parameter helps in limiting the number
of indexes to be generated by selecting only those
that are the most frequently used by the workload.
At each step corresponding to the value of minsup,
the mean response time of the input workload is
computed.
Q1: SELECT * FROM T1, T2 WHERE A BETWEEN 1 AND 10AND C=D
Q2: SELECT * FROM T1, T2 WHERE B LIKE THIS%' AND C=5 AND E<100
Q3: SELECT * FROM T1, T2 WHERE A=30 AND B>3 GROUP BY C HAVING SUM(E) >2
Q4: SELECT * FROM T1 WHERE B>2 AND E IN (3, 2, 5)
Q5: SELECT * FROM T1, T2 WHERE A=30 AND B>3 GROUP BY C HAVING SUM (E)>2
Q6: SELECT * FROM T1, T2 WHERE B>3 GROUP BY C HAVING SUM (E)>2
Figure 5: Sample Workload.
// Cold run (no timing)
FOR
each query in the workload DO
Execute current query
END FOR
// Warm run
FOR
i = 1 To number of replications DO
FOR
each query in the workload DO
Execute current query
Compute response time for current query
END FOR
END FOR
Compute global mean response time and confidence interval
Figure 6: Test Protocol Algorithm.
Figure 7: Combining ODS and DW into the same structure
The ODS (Operational for OLTP) Workload
0
20
40
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8 9 10111213141516171819202122
Query/Snapshot
Workload (sec.)
The DW (Analytical for DSS) Workload
0
20
40
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Query/Snapshot
Workload (sec.)
Figure 7: Combining ODS and DW into the same structure.
COMBINING THE DATA WAREHOUSE AND OPERATIONAL DATA STORE
285

3.2 Experiments' Results
The results obtained are presented in figures 7 and
8. The results from figure 7 correspond to
combining ODS and DW (Bhargava et al., 1999)
into the same structure.
Figure 7 shows that there are very different
workload patterns for the ODS environment and the
DSS environment. The ODS workload is one where
there are peaks and valleys, but where the mean
system utilization is a descriptive number. Peaks and
valleys depend on the needed response time for
computing the resultant answer for the query or
snapshot process. The ODS workload is one that can
be predicted and managed, and the DW workload is
essentially a binary workload. Either the system is
being used heavily or not being used at all. This
workload is not predictable and the mean utilization
of the system is a useless number in most
circumstances. When the ODS and DW are mixed in
the same environment and technology, the two
workloads are forced into the same box. Figure 8
shows what happens when the ODS and the DW
(Awad, 2000) are mixed in the same environment
and technology, the two workloads are forced into
the same box. As shown in left side diagram in
figure 8 that when combining the two workloads in
a large processor (IBM S400 machine) the end user
will be happy with the response time but the price is
as follows: the unit price of the hardware is as
expensive as it gets; the organization has excess
capacity left over; and finally, the financial manager
considers the purchase to be a disaster because it
doubles the expenses minimally several ten times of
cost compared with the other solution (see table 1).
On the other hand, the right side diagram in figure 8
shows that when combining the two workloads in a
small processor (PC Pentium III, 500 Server) the
end user is suffering a lot of a long response time. In
fact the use of this alternative approach may
appreciate the finance people (especially when they
find out that the machine is being used close to 100
percent of the time). However, the following
problems have been arisen when a small machine is
used to contain a mixed workload:
a. Response Time
The end user doing ODS simply is unable to cope
with the unpredictable and unacceptable levels of
response that are achieved (response time is
increased by triple in some cases of analytical
analysis and more in several other cases).
b. Style Incompatibility
Table 1: Tentative Expenses Cost Comparison between Large and Small Processor Machines.
Large processor such as IBM S400
or equivalent
Small processor such as PC Pentium III 500
Server or equivalent
Hardware estimated price in US $ Minimum 40,000 Minimum 1,500
Software price in US # Minimum 15,000 Minimum 1,000
Users Higher experienced and qualified
users
Normal experienced users
Technical support Essentially needed and expensive
(100 US $ per visit)
Rarely needed and cheap (10 US $ per visit)
A Large Processor (IBM-S400 machine) A Small Processor (Pentium III, 500 Server)
Figure 8: Large Processor Vs a Small Processor
The DW (Analytical for DSS) Workload
0
20
40
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Query/Snapshot
Workload (sec.)
The DW (Analytical for DSS) Workload
0
20
40
60
80
100
120
140
160
180
12345678910111213141516171819202122
Query/Snapshot
Workload (sec.)
The ODS (Operational for OLTP) Workload
0
20
40
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8 9 10111213141516171819202122
Query/Snaps hot
Workload (sec.)
The ODS (Operational for OLTP) Workload
0
20
40
60
80
100
120
140
160
180
1 2 3 4 5 6 7 8 9 10111213141516171819202122
Query/Snapshot
Workload (sec.)
Figure 8: Large Processor Vs. a Small Processor.
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The system cannot be tuned or optimized for any
style of processing. This workload incompatibility is
solved by separating the ODS and the DW (Little
and Gibson, 2003).
c. Mixing of Communities
When the ODS and the DW are mixed, the DSS
(Vahidov, 2002) analytical community is thrown in
with the clerical community. Unfortunately, when
enough people start to use the combined structure,
they start to "step on each other's together".
d. Data Flow are Incompatible
When there is a combined workload, data flow
across the communication lines are mixed as well. In
order to achieve a uniform and efficient flow, the
separation of the different data flow types is must
needed.
e. Mixed Current and Historical Data
When current data is mixed with historical data
(Jennex et al., 2003), the problem of the difference
in the probability of data access arises. Current data
typically has a much higher probability of access
than historical data. But when they are mixed
together, current data can hide behind historical
data, making current data hard and inefficient to
access.
f. Overhead of Update
The overhead of update shows up as check pointing,
rollback, logging of transactions, and committing
data. When update is a possibility all transactions
that are in execution pay the same price of overhead.
When the ODS is separated from the DW (Harada et
al., 2004), separating update processing from access
processing is urgently needed. Update processing is
regularly done in the ODS environment while
access-only processing (which is very efficient) is
done in the DW environment.
g. No Optimal Hardware Architecture
When the ODS and the DW are mixed, there is no
optimal hardware architecture. When the ODS is
separated from the DW, the ODS environment has a
particularly strong affinity for Multiple Processor
Platform (MPP) architecture: multiple units of data
are tied together by a common backbone, in other
words, ODS environment operates optimally on
MPP architecture. Depending on the size and the
processing done, the DW may or may not run
optimally on MPP architecture. Indeed, the DW may
operate optimally on Shared Memory Platform
(SMP) architecture: multiple processors are tied
together in a shared memory configuration. When
this is the case, the mixing of the two environments
causes a dilemma for the CIF (Brendt et al., 2001)
architecture. These are the reasons why the ODS
and the DW need to be split. They can violate the
CIF (Schwarz et al., 2001) and be combined, but if
this is done, the CIF architecture needs to be aware
that there are prices to be paid.
4 CONCLUSION AND FUTURE
WORK
Attempts to combine the ODS with the DW by some
small business organizations aiming to save
expenses is limited and poor idea. As the
experimental results proved that the obstacles and
the drawbacks of combining the ODS and DW
destroy the aims of saving expenses but makes the
organization to pay the price a lot. The drawback
and obstacles of combining the ODS and the DW
are severe and might arise in different ways. In case
of using forced combination of incompatible
transaction types, the experimental results showed
that the following problems have been arisen: system
blocks cannot be optimized for one type of
transaction or the other; buffers cannot be
optimized for one type of processing or the other;
system initialization parameters, such as
FREESPACE cannot be optimized for one type of
activity or the other; and finally, data cannot be
distributed across system resources (CPU, disk, etc.)
for one type of processing or the other. In case using
a small machine such as PC Pentium III 500 to
house a mixed workload, the experimental and
analytical results proved that the following problems
have been arisen: much increase in response time,
the system cannot be tuned or optimized for any
style of processing (style incompatibility), mixing of
communities, data flow are incompatible, mixed
current and historical data, overhead of update, and
finally, no optimal hardware architecture. In case of
using a large processor such as IBM S400 machine
the response time is much improved compared with
using small machine (PC Pentium III 500) but the
price is very severe and the other problems that are
mentioned above are still as they are. Finally, the
experimental and analytical results conclude that the
ODS and the DW need to be physically separate
entities and environments in order to ensure long-
term viability of the corporation information
ecosystem. Future work is developing different
hardware platforms and hardware architectures
particularly the unit-processor architectures (a single
storage device is controlled by a single processor) to
work separately better in the ODS, DW, and data
marts environments.
COMBINING THE DATA WAREHOUSE AND OPERATIONAL DATA STORE
287

REFERENCES
Awad H. K, 2000, A Multi-Entry Methodology for Data
Warehouse Develop. Proc. of 8
th
Cairo Int. Conf on AI
Applications, Cairo, Egypt, pp. 21-22.
Dhor Vasant and Stein Roger, 1996, Seven Methods for
Transforming Corporate Data into Business
Intelligence, Prentice Hall PTR, London, UK
Donald J. Brendt, John W. Fisber, Alan R. Hevner, 2001,
Healthcare Data Warehousing and Quality Assurance,
Computer, December 2001, Vol. 34, No. 12, pp. 56-
65, IEEE Computer Society, Piscataway, NJ, USA
Farzad Shafiei, David Sundaram, 2004, Multi-Enterprise
Collaborative Enterprise Resource Planning and
Decision, Proceedings of the 37th Annual Hawaii
International Conference on System Sciences
(HICSS'04) - Track 8, pp. 80228a, January 05 - 08,
2004, Big Island, Hawaii.
Harry Singh, 1999, Interactive Data Warehousing,
Prentice Hall, Inc, A Simon & Schuster Company,
Upper Saddle River, NJ 07458, USA
Hemant K. Bhargava, Suresh Sridhar, and Craig Herrick,
1999, Beyond Spreadsheets: Tools for Building
Decision Support Systems, Computing Practices,
March 1999, pp. 31-39, 0018-9162/99 @ 1999 IEEE.
Holger Schwarz, Ralf Wagner, Brenhard Mitschang, 2001,
Improving the Processing of Decision Support
Queries: The Case for a DSS Optimizer, 2001,
International Database Engineering & Applications
Symposium (IDEAS-01), Jul-01, pp. 0177.
Inmon W. H., Imhoff Claudia and Sousa Ryan, 1998,
Corporate Information Factory, Wiley Computer
Publishing, New York, USA
Kemal A. Delic, Laurent Douillet, Umeshwar Dayal,
2001, Towards Architecture for Real-Time Decision
Support Systems: Challenges and Solutions, 2001
International Database Engineering & Applications
Symposium (IDEAS-01), Jul-01, pp. 0303.
Lilian Harada, Yuuji Hotta and Tadashi Ohmori, 2004,
Detection of Sequential Patterns of Events for
Supporting Business Intelligence Solutions,
International Database Engineering and Application
Symposium (IDEAS'04), July, 07-09, 2004, pp. 475-
479, Coimbra, Portugal , IEEE, Piscataway, NJ,
USA.
M. Poess, B. Smith, L. Collar, and P. A. Larson, 2002,
TPC-DS: Taking Decision Support Benchmarking to
the next level. In 2002 ACM SIGMOD International
Conference on Management of Data, Madison, USA
Michael Blaha, 2001, Data Warehouses and Decision
Support Systems, Computer, December 2001, Vol. 34,
No. 12, pp. 38-39, IEEE Computer Society,
Piscataway, NJ, USA
Murray E. Jennex, Lorne Olfman and Theophilus B. A.
Addo, 2003, the Need for an Organizational
Knowledge Management Strategy, 36th Annual
Hawaii International Conference on System Sciences
(HICSS'03) - Track 4, pp. 117a, January, 06 - 09,
2003, Big Island, Hawaii.
N. Bolloju, 2001, Extended Role of Knowledge Discovery
Techniques in Enterprise Decision Support
Environments, 34
th
Annual Hawaii International
Conference on System Sciences (HICSS-34) - Volume
3, Jan-2001, pp. 3012.
R. Vahidov, 2002, Decision Station: A Notion for a
Situated DSS, 35
th
Annual Hawaii International
Conference on System Sciences (HICSS' 02), Vol. 3,
pp. 78b, January, 07-10, 2002, Big Island, Hawaii.
Rob Mattison, 1996, Data Warehousing, Irwin McGraw-
Hill, New York, USA
Robert Grover Little, Michael Lucas Gibson, 2003,
Perceived Influences on Implementing Data
Warehousing, IEEE Transactions on Software
Engineering, April 2003 (Vol. 29, No. 4), pp. 290-296,
IEEE, Piscataway, NJ, USA.
Seoudi Group, 1998, SG Manual Reference, SG
Documents, by permission from GM.
Singh Harry, 1999, Data Warehousing: Concepts,
Technologies, and Implementation Prentice Hall PTR,
London, UK.
Svetlozar Nestorov and Nenad Jukic, 2003, Ad-Hoc
Association-Rule Mining within the Data Warehouse,
36th Annual Hawaii International Conference on
System Sciences (HICSS'03) - Track 8, pp. 232a,
January 06 - 09, 2003, Big Island, Hawaii.
T. Hess, J. Wells, 2002, Understanding How Metadata and
Explanations Can Better Support Data Warehousing
and Related Decision Support Systems: Exploratory
Case Study, 35
th
Annual Hawaii International
Conference on System Sciences (HICSS-35) - Volume
8, Jan-2002, pp. 223.
Transaction Processing Council, 1999, TCP Benchmark R
standard Specification
.
ICEIS 2006 - DATABASES AND INFORMATION SYSTEMS INTEGRATION
288