UNIVERTY OF DAR ES SALAAM

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              UNIVERTY OF DAR ES SALAAM


RESEARCH PROPOSAL FOR THE MASTERS OF SCIENCE IN
        COMPUTER SCIENCE DEGREE BY THESIS


                       STAGE: I & II


1.0. NAME OF CANDIDATE:      LUNGO, JUMA H.
                             Reg.No: HD/TP.1/2000
                             B.Sc. (Comp.) (Hons.) (DAR)


2.0. NAME OF SUPERVISOR: 1. Dr. S. C. N. Kitinya
                             2. Dr. H. M. Twaakyondo


3.0. DEPARTMENT AND FACULTY: DEPARTMENT OF
    COMPUTER SCIENCE – FACULTY OF SCIENCE.


4.0. PROPOSED DEGREE: M.Sc. (COMPUTER SCIENCE)


5.0. TITLE:
    Design And Implementation of a Data Warehouse
    Prototype For The Chief Academic Officer, University Of
    Dar Es Salaam Within The Context of Relational Online
    Analytical Processing (Data Analysis).
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6.0    INTRODUCTION
6.1    GENERAL INTRODUCTION
IBM first published a technical article on information warehouse strategy
in 1988 (Ballard, Chuck. 1996). This is a strategy for satisfying business
needs for complex queries and insightful information with a managed
database. In 1990, William Inmon (Inmon, W. H. 1997) coined he phrase
“Data warehouse”. The ultimate goal of data warehousing is the creation
of a single, logical view of data, which may reside in many physically
disparate databases (Butler Group. 1996). “…traditional database
systems are good at recording and reporting what happened. A data
warehouse shows why” (Fisher, Lawrence. 1996).


Data warehouses represent the latest great paradigm of database
management. The earliest data management systems were hierarchical,
run on massive mainframes, and were used primarily for archival
purposes. The first big change came in the early 1980’s, with the
adoption   of   relational   database       systems,    which   have     primarily
operational applications. These systems, typically run on minicomputers,
are used for online transaction processing (O.L.T.P.), for example, to
operate networks automated teller machine. Now come Data warehouses,
commonly run on client/server networks of personal computers and
more powerful server machines. These latest systems are used for online
analytical processing (O.L.A.P.), an essentially strategic application.


Data   warehouse    organize   and      store   data,   from    the    operational
environment, over a long historical time perspective. Consequently, they
                                      3


provide data found in the operational environment. Data warehouse
allows user to recognize data they want and, using simple query tools,
create their own queries, based on solid repository of integrated,
historical data.


The concept of data warehouse is that: It’s a place where data extracted
from production systems in the enterprise is stored (Warner, Tim. 1995).
The University of Dar es Salaam as a big organization, there are
operational systems like: Admission systems, Accommodation system,
Examination record system, Master timetable, etc. all of these systems
generate data that are vital to the University decision makers. Data
warehouse is required to organize all of these data to be readily
accessible and meaningful to the Chief Academic Office to support their
decisions making.


This study is divided into two main parts. The first part of the study will
involve literature study, and documentation of the architecture, planning
and designing methods, implementation techniques and laying out
options for data ware house. This part of the research will be carried out
and documented to enhance future references. The second part of the
research will be that of laboratory work. This will involve the real
development of the prototype of Data warehouse within the context of
Relational Online Analytical Processing (ROLAP).


6.2. STATEMENT OF THE RESEARCH PROBLEM:
The frustrations of the 1970s are felt more keenly today, because the
technology that facilitating sharing of data (network, communication
protocols,   sophisticated   Database     Management     Systems,   Decision
support systems, etc.) are freely available, yet organizations still find that
                                     4


data is organized into functional silos, from which it is hard to extricate
what you want in other, related function (Jack D. Doyle. 1997).
At the University of Dar es Salaam, despite the availability of more and
more powerful computers on everyone’s desk and communication
networks, large number of executives and decision makers can’t get their
hands on critical information that already exist in the University. One of
the executives of the University is the Chief Academic officer. As an
education institution, the University every day creates data about
students, supporting programmes, staff etc, of which are important in
supporting the daily works of the Chief Academic office of the University,
but for the most part, this data is locked up in a myriad of manual and
computer systems and is exceedingly difficult for the chief academic
officer to get at.


We are intending to conduct a study to analyse, design and implement
data warehouse that will enable high improvement of information access
for the Chief Academic Office.


According to Michael Haisten, 1998 the most powerful justifications for
opting Data warehouse investment in the Chief Academic office therefore
are:
      Quality goals, since its typical objective are improving information
       access,
      Bringing the user in touch with their data,
      Enhancing the quality of their decisions and
      Providing cross-function integration of operation systems within
       the Organisation.
                                       5


The result obtained will then be useful for future development of
successful Data warehouse of the Chief Academic office of the University
of Dar es Salaam.


6.3. RESEARCH OBJECTIVES
The general aim of the research is to study the architecture, design and
implementation of Data warehouses by developing a model for Chief
Academic Office Data warehouse.


The proposed research objectives, derived from this general aim are:
      To study and document the architecture of Data warehouse,
      To determine (identify) aspects, playing key roles in the design and
       implementation of data warehouse,
      To develop a University system model (prototype) for Data
       warehouse,
      To test (validate) the model in a real life cases.


6.4    SIGNIFICANCE OF THE STUDY
The result obtained from this research will be used to develop Data
warehouse for the Chief Academic Office of the University. Also the
documentation (report) of the research will be used as reference for any
other study on the topic of Data warehouse especially from the University
of Dar es Salaam.


This study too will encourage and challenge many organisation to opt for
data warehouse investment in order to improve information access
within their firms, bringing the user of their information in touch with
their data, and providing cross-function integration of operation systems
within their organisation. Data warehouse for the Chief Academic Office
                                      6


will enable the decision makers to access data, understood the data and
manipulate them while making decisions for the UDSM.


6.5   LITERATURE REVIEW
Data warehouse is defined as a subject – oriented, integrated, time
variant, non-volatile collection of data in support of      management’s
decision – making process (Inmon, W.H. 1996). Subject-Oriented means
the data warehouse focuses on the high-level concerns of the business,
as in contrary to operational systems, which deals with process, e.g.,
order processing, Billing system etc. Integrated implies that data being
stored in a consistent format. Time variant means each data point is
associated with a point in time. And non-volatile means the data does
not change once it gets into the warehouse (Jack D. Doyle. 1997).


Ken Orr, 1996 stated that Data warehouse is a field that grows out of
integration of a number of different technologies and experiences over the
last two decades. Data warehouse can be best represented as an
enterprise-wide framework for managing informational data within the
organization. There are two fundamentally different types of information
systems in all organizations namely Information systems and Operation
systems.


Operational systems are the systems that help us run the enterprise on
day to day activities (Ken Orr. 1996). The University of Dar es Salaam
has systems like Admission system, examinations record systems,
accommodation    system,   Payroll,   Timetable   etc.   Because   of   their
importance to the University, operational systems were almost always
the first to be computerized. Indeed, most large organizations couldn’t
operate without their operational systems and data that these systems
maintain. Other functions within the organization have to do with
                                      7


planning, forecasting and managing the organization. These are the
knowledge-based functions, which form the Information system of the
organization. Information systems have to do with analyzing data and
making decision, often major decisions about how the enterprise will
operate, now and in the future. Information data needs often span a
number of different areas and needs large amounts of different
operational data that are in summary form.


Data warehouse provide information to the knowledge-based function
(Decision Support Systems) within the organization. The operational
systems generate data that have to be put and organized to the data
warehouse (Vince Desio). Consider fig.1: below.
                Fig.1: The concept of data warehouse.




           (Source: http://www.datawarehouseconsulting.com/img2.gif)
A Data warehouse can be physically centralized, logically centralized but
physically distributed, or simply distributed. With today’s powerful Local
Area Network based Database servers, data warehouse can also take
advantage of the benefits of distributed computing.
                                              8


Building a data warehouse is essentially a complex integration effort.
Literally hundreds of system components must be brought together to
work as an integrated application (Vince Desio. 1998). The graphic on the
next page below represents only a high-level view of the basic
components that comprise a Data warehouse.
                        Fig.2: Data Warehouse Components


                                   ADMINISTRATION




                                        DATA WAREHOUSE
                                          REPOSITORY                            Deskttop
                                                                INFORMATION
                                                                                Desk op
INTERNAL &
INTERNAL &                                 RDBMS
                                                                                       Tools
                                                                                       Tools
     EXTERNAL
     EXTERNAL           SOURCING
                        SOURCING                                  ACCESS
 OPERATIONAL DATA                          Physical Meta
 OPERATIONAL DATA
 OPERATIONAL DATA    Transformati          Data                                   Query
                           on                                   Middleware
                                           Mult-                                  OLAP
                      Metadata                                 Performance
                                            Dimensional
                                                                 Management        WWW
    Warehouse       Integration
     Meta Data                                                  Abstractions      Report
                     Conditioning                                                 Graphics
    System of       Aggregation                               User object
     Record                                                     Preparations      Spread
    Models              Initial vs.                                               Sheet
    Stewardship
                             Change                                                Meta data
                            Load                                                    Catalog




                                            METADATA
                                            METADATA

                DATA WAREHOUSE ARCHITECTURE.
A Data warehouse architecture is a way of representing the overall
structure of data communication, processing and presentation that
                                          9


exists for end user computing within the enterprise. The architecture is
made up of a number of interconnected parts (The Ken Orr Institute;
revised edition, 2000):
· Operational Data Base / External Data Base Layer
· Information Access Layer
· Data Access Layer
· Data Directory (Metadata) Layer
· Process Management Layer
· Application Messaging Layer
· Data Warehouse Layer
· Data Staging Layer


Operational Data Base / External Data Base Layer
The goal of data warehousing is to free the information that is locked up
in the operational data bases and to mix it with information from other,
often external, sources of data. Increasingly, large organizations are
acquiring additional data from outside data bases. This information
includes demographic, econometric, competitive and purchasing trends.
The so-called "information superhighway" is providing access to more
data resources every day.


Information Access Layer
The Information Access layer of the Data Warehouse Architecture is the
layer that the end-user deals with directly. In particular, it represents the
tools that the end-user normally uses day to day, e.g. Excel, Word,
Access, PowerPoint, SAS, etc. This layer also includes the hardware and
software involved in displaying and printing reports, spreadsheets,
graphs and charts for analysis and presentation.
Data Access Layer
The Data Access layer of the Data Warehouse Architecture is involved
with allowing the Information Access layer talk to the Operational Layer.
                                          10


In the network world today, the common data language that has emerged
is SQL. The Data Access layer then is responsible for interfacing between
Information Access tools and Operational Data Bases.


Data Directory (Metadata) Layer
In order to provide for universal data access, it is absolutely necessary to
maintain      some    form    data    directory    or   repository     of   meta-data
information. Meta-data is the data about data within the enterprise. In
order to have a fully functional warehouse, it is necessary to have a
variety of meta-data available, data about the end-user views of data and
data about the operational data bases.


Process Management Layer
The Process Management layer is involved in scheduling the various tasks that must be
accomplished to build and maintain the data warehouse and data directory information.
The Process Management layer can be thought of as the scheduler or the high level job
control for the many processes (procedures) that must occur to keep the Data Warehouse
up-to-date.
Application Messaging Layer
The Application Message layer has to do with transporting information
around the enterprise-computing network.


Data Warehouse (Physical) Layer
The (core) Data Warehouse is where the actual data used primarily for
informational uses occurs.


Data Staging Layer
Data staging is also called copy management or replication management,
but in fact, it includes all of the processes necessary to select, edit,
summarize, combine and load data warehouse and information access
data from operation and/or external databases.
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The knowledge of Data warehouse in Tanzania is new. Currently there is
no known Data warehouse in Tanzania. This research will then create
awareness to the Tanzanian IT professionals and society in general to
utilize the power of data warehouse especially at higher learning
institutions like in the Universities where all necessary facilities for
building Data warehouses are present.


6.6    RESEARCH HYPOTHESIS
      The architecture of the Data warehouse can be studied and
       documented to become standard and known to every one
       developing data warehouse.
      There   are   key   issues   playing     roles    in   the    design     and
       implementation of data warehouse that need to be determined.
      The existing expertise and computer facilities at the University can
       facilitate to develop a data warehouse.
      The resulting Data warehouse Model could be tested in a real case
       in order to evaluate its completeness.




7.0    METHODOLOGY
7.1    Study Area
The University of Dar es Salaam was born out of a decision taken on
March 25th, 1970, by the East African Authority, to split the then
University of East Africa into three independent universities for
Kenya, Uganda and Tanzania.


The University of Dar es Salaam consists of six faculties, five institutes
and two colleges: Faculty of Arts and Social Sciences; Faculty of
Commerce       and   Management;    Faculty      of     Education;    Faculty    of
                                             12


Engineering;         Faculty    of   Law;     Faculty    of   Science;      Institute   of
Development Studies; Institute of Kiswahili Research; Institute of Marine
Sciences;      Institute       of    Production         Innovation;      Institute      of
Resource Assessment; the University College of Lands and Architectural
Studies and the Muhimbili University College of Health Sciences. The
University also operates a Computing Centre, a Library and four
bureaus: the Economic Research Bureau in the Faculty of Arts and
Social Sciences; the Bureau for Educational Research and Evaluation in
the    Faculty        of    Education;            the    Bureau       for     Industrial
Cooperation     in    the   Faculty     of    Engineering      and     the    University
Consultancy Bureau.


The University is situated on the west side of the city of Dar es Salaam,
occupying 1,625 acres on Observation Hill, 13 k.m. from the centre of
the city of Dar es Salaam.


For purposes of maintaining East African inter-university academic
cooperation and communication, an Inter-University Council for East
Africa was set up in 1970.                   The Council has established an
Inter-University Exchange Programme, through which the University
admits students from other East African countries mainly Kenya and
Uganda.     The University also admits students from several other
countries   the       world-over     through        established      links,    exchange
programmes or individual applications. Most of these students receive
their bursaries from their respective governments. Students from other
countries are considered for admission to both undergraduate and
postgraduate studies, subject to the availability of vacancies.
                                     13


7.2   Methodology
A short visit will be made to the Chief Academic Office. This visit is
intended to familiarize the researcher and the stakeholders and also will
enable an initial study of      how information flows in and out of the
CACO’s office.


7.2   Data Collection techniques
Observation
The aim of including this data collection technique is to conduct a
detailed notation of behaviors, events and the contexts surrounding the
Chief Academic Office. To fulfill this, physical observations of what tools
the CACO have that are used to collect analyze and disseminating
information will be conducted.


Interviews
An interview will be held between the researcher and the Chief Academic
Office staff. The purpose of interview is to find out what is in or on some
else’s mind (John W. Best & James V. Kahn. 1993). Questions will be
designed in such a way that it will enable us to capture most information
we need that will help us in our research.


Case Study
Case study should help in “capturing the knowledge of practitioners and
developing theories from it”.
A case study methodology is well suited to identify key events and actors
and to linking them in a casual chain.
The case strategy is particularly well suited to IS research because the
technology is relatively new and interest has shifted to organizational
rather than technical issues.
Case study is chosen because of its abilities to:
                                     14


    Give the possibility to generate theories from practice (as a
      preparation stage for developing the model of Data warehouse);
    Allow to understand the nature and complexity of the processes
      taking place in Data warehouse;
    Research an area in which few previous studies have been carries
      out;
    Research an area in which it is necessary to measure variables,
      but there is no a priori knowledge of what the variables of interest
      will be. In this case the variables are aspects, which are necessary
      to determine and estimate their role.


7.3   EXPECTED RESULTS OF THE RESEARCH


Theoretical Results
The main theoretical result of the research will be the model, which
supports Design and implementation of Data warehouse. The model
should comply with the ongoing Information Plan Policy (IPP) at the
University of Dar es Salaam. The model could include methods,
techniques and/or instrumentation, which have to be able to support the
Design and Implementation of Data warehouses in Tanzania.


Practical results
The main practical result of the research should be the realization of the
Design and implementation of Chief Academic office Data warehouse
of the University of Dar es Salaam. The success of this part of the
research depends on the full support and willingness of the technical
staff and management of already installed systems to realize that this
research will help in their daily needs of information.
                                    15


8.0   REFERENCE/BIBLIOGRAPHY:
      1.   Jack D. Doyle.(1997). Informed Decision Making Through
           Data warehousing.
           http://dhrinfo.hr.state.or.us/intranet/tands/Dwpap/DWWHITEP.htm

      2.   Vince Desio. Data warehouse Components.
           http://www.datawarehouseconsulting.com/page3.html
      3.   Ken Orr. (1996). Data warehousing Technology. The Ken Orr
           Institute; revised edition, 2000.
      4.   Roger Burlton. (1998). Data warehousing in the Knowledge
           Management Cycle. http//datawarehouse.dci.com/articles.
      5.
      6.   Ralph Kimball The Data warehouse Life Cycle Toolkit
      7.   Building the Data warehouse by William H. Inmon
      8.   Data warehouse Design Solutions by Christopher Adamson,
           Michael Venerble.
      9.   SQL Server 7 Data warehousing by Michael Abbey, Ian
           Abramson, Larry Barner, be Taub, Michael J. Corey.
      10. High performance Oracle Data warehousing by Donald
           Burleson.
      11. Data Preparation for Data Mining by Dorian Pyle
      12. Data warehousing: Architecture and Implementataion by
           Mark Humphries, Michael w. Hawkins, Michelle C. Dy.
      13. Butler Group. 1996. Business Case for Data Warehousing.
           Strategies and Technologies. October 1996, Butler Group,
           UK.
           http//www.butlergroup.co.uk/manguide/dwuk1096/conten
           ts.htm.
      14. Fisher, Lawrence. 1996. Along the Infobahn. Data
           Warehouses. Third Quarter, 1996. Strategy & Business,
                            16


     BoozAllen & Hamilton Inc. http//www.strategy-
     business.com/technology/96308/page1.html
15. Boar, Bernard (Bernie). 1996. Understanding Data
     Warehousing Strategically. White paper commissioned by
     NCR's Communication Industry Line of Business. June 14,
     1996. The Data Warehousing Institute, Gaithersburg, MD.
     http://www.tekptnr.com/tpi/tdwi/review/bboar1.htm.pp.25
16. Imirie, Peggy. 1996. Your Data Warehouse: A Business
     Success or Science Project? Lesson from the Experts. 29
     December 1996. The Data Warehousing Institute,
     Gaithersburg, MD. http://www.dw-
     institute.com/lessons/sciproj.htm. pp. 2.
17. Ballard, Chuck. 1996. Strategies to make your Data
     Warehouse a Success. Lesson from the Experts. December
     29,1996. The Data Warehousing Institute, Gaithersburg,
     MD. http://www.dw-institute.com/lessons/strateg.htm
     pp.2.
18. Byte. 1997. Architectural Distinctions. June 1997.
     http://www.byte.com/art/9706/sec20/art4.htm.
19. Eckerson, Wayne W. 1994. Implementing Access to
     Distributed Data Using a Data Warehouse Strategy. Patricia
     Seybold Group, Distributed Computing Monitor Case Study,
     September 1994.
     http://www.psgroup.com/cases/1994/cs994d.htm.
20. Barbara, Gaskin 1998. Realizing the Strategic Value of
     Data Warehouses (Decision Support Technology).
                                    17




      9.0    OTHER INFORMATIONS:
      9.1:   Financial Requirement:
      The proposed study is to be financed by the University of Dar es
      Salaam. The technical assistance and equipment facilities will be
      provided by the Department of Computer Science.


      9.1.1:       BUDGET:
      (a).   University costs:
       DESCRIPTION               YEAR 1      SUBSQUENT YEAR    SPONSOR
Tuition fees                     950,000/=         950,000/=    UDSM
Application fee                   10,000/=                 -     -do-
Registration fee                  20,000/=                 -     -do-
Thesis Supervision               200,000/=         200,000/=     -do-
Medical capitation fee           100,000/=         100,000/=     -do-
Special Faculty Requirement      100,000/=         100,000/=     -do-
Research Field Cost              750,000/=                 -     -do-
                       TOTAL   2,130,000/=       1,350,000/=     -do-


      (b).   Student costs:
       DESCRIPTION               YEAR 1      SUBSQUENT YEAR    SPONSOR
Caution money                      2,000/=                -    UDSM
Student Union                      1,200/=          1,200/=     -do-
Books                            300,000/=        300,000/=     -do-
                                           18


Stationary                              50,000/=           50,000/=       -do-
Thesis Production                              -          150,000/=       -do-
Stipend (based on
130,000/=per month)               1,560,000/=           1,560,000/=       -do-
                  TOTAL           1,913,200/=           2,061,200/=       -do-

       9.1.2: RESEARCH/FIELD AND MATERIAL COSTS (Computer Lab.)
Up-keep allowance and transport                                         530,000/=
Processing fee                                                          120,000/=
Electrical and electronics components                                   100,000/=
                                                            Subtotal    750,000/=




       9.1.3: RESEARCH PROPOSAL PRODUCTION:
Paper 5rims @ 5,000/=                                                    25,000/=
Secretarial services, 30 pages @ 600/=                                   18,000/=
Photocopy, Department level 30 pages @40/=, 20 copies                    24,000/=
Photocopy, Faculty level 30 pages @40/=, 20 copies                       24,000/=
Photocopy, Senate level, 30 pages @40/=, 20 copies                       24,000/=
                                                            Subtotal    115,000/=

       9.1.4: THESIS PRODUCTION
Paper 5rims @ 5,000/=                                                     15,000/=
Secretarial services 250 pages @ 600/=                                    15,000/=
Diskettes 3 boxes @ 5,000/=                                               15,000/=
Photocopy, 250 pages @40/=, 4 copies                                      40,000/=
Loose bound 4 copies @ 5,000/=                                            20,000/=
Final binding 4 copies @ 6,000/=                                          24,000/=
                                Subtotal                                 264,000/=
                                                           TOTAL       1,129,000/=
19
                                                                                       20



             9.2:   RESEARCH SCHEDULE


ACTIVITY                                           2000/2001                                                                     2001/2002
                Nov.   Dec.   Jan.   Feb.   Mar.    Apr.   May   Jun.   Jul.   Aug.   Sep.   Oct.   Nov.   Dec.   Jan.   Feb.   Mar.   Apr.   May   Jun   Jul.   Aug.   Sep.
Registration,
literature
review,
Research
Proposal.
Data
warehouse
planning,
Analysis and
Design.
Data
warehouse
Implementat
ion and
Testing.
Thesis
write-up,
production
&
submission.
                                                          21


9.3:       COMMENTS


Date:...............................................................................Signature:...........................
                                       Name:                LUNGO, J. H. (Reg.No: HD/TP. 1/2000)
                                                                                                            (candidate)


Supervisor's Comments.
....................................................................................................................................
....................................................................................................................................
....................................................................................................................................
Date:..............................................................................Signature:............................
                                                                                       Name:
                                                                                                            (Supervisor)


Head of Department's Comments
....................................................................................................................................
....................................................................................................................................
....................................................................................................................................
....................................................................................................................................
Date:.........................................................................Signature:.................................
                                                                    Name:              Dr. H. Twaakyondo
                                                           The Head, Department of Computer Science.

				
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