ISTAfrica_Paper_ref_112_6583

IST-Africa 2014 Conference Proceedings
Paul Cunningham and Miriam Cunningham (Eds)
IIMC International Information Management Corporation, 2014
ISBN: 978-1-905824-43-4
Copyright © 2014 The authors www.IST-Africa.org/Conference2014 Page 1 of 7
Development of a Data Model for Semantic
Exploitation of Municipality Records in
South Africa
Kgotatso Desmond MOGOTLANE1, 2
, Jean Vincent FONOU DOMBEU1
1
Vaal University of Technology, Private Bag X021, Vanderbijlpark, 1900, South Africa
Tel: +27 (0) 16 950-9831, Fax: +27 (0) 16 950-9497,
Email: {desmond.mogotlane, fonoudombeu}@gmail.com
2
ArcelorMittal South Africa, Delfos Boulevard, Vanderbijlpark, 1911, South Africa
Tel: +27 (0) 16 889-5040, Fax: +27(0) 86 624-7372,
Email: desmond.mogotlane@arcelormittal.com
Abstract: A number of South African municipalities have embraced the use of
advanced information technology to improve their services to citizens. However, this
is not a common practice across the country; there are still municipalities within
South Africa who have not automated their information systems for better service
delivery to the public. This study develops a data model that can be applied by
municipalities across South Africa as the basis for automating their information
systems for service delivery. A combination of qualitative and quantitative methods
is used. Preliminary data is collected using: (1) literature review, (2) observation at a
selected local Municipality and (3) online related resources at two additional
municipalities. The collected data is analysed and a generic conceptual municipality
data model is drawn. Finally, the data model is further implemented in Oracle to
create a test database for future Semantic Web processing.
Keywords: South Africa, Municipalities, Relational Data Model, Ontology,
Semantic Web
1. Introduction
1.1 Presentation of the domain
In South Africa, the Government has a constitutional obligation to provide basic services to
its citizens [1]. South African municipalities are at the fore-front of delivering services such
as the provision of potable water, sanitation, refuse removal, property assessments and
electricity to the public [2, 3]. To achieve effective local Governance, South Africa is
divided into a number of municipalities to ensure that they serve all areas under their
jurisdiction [4]. Municipalities offer their services at a cost to the public depending on their
residence profiles. Municipalities have tariff policies to govern the billing of major services
and consumables such as electricity, water, sewerage, and refuse removal. All
municipalities are obliged to act under certain laws such as the Municipal Systems Act of
2000 [5]. These acts regulate the activities of municipalities to ensure that they remain
constitutional when dealing with the public. The relationship between a municipality and
the public can be compared to that of a service provider and customer. This is because the
customer (public) will interact with the service provider (municipality) to request and to
pay for certain services rendered. The next sub-section provides a background on the
adoption of ICT in South African municipalities.

1.2 Information Technology Adoption in South African Municipalities
Some of the larger South African municipalities are beginning to embrace the use of
advanced information technology to improve their services to the citizens. For example, the
eThekwini Municipality in Durban is running a project to install Ethernet infrastructure in
the Durban area. This project is aimed at supporting an array of e-Government services
such as smart metering, self-service facility for accounts and statements, and sharing of
information amongst households [6]. The Municipality of the City of Johannesburg has also
introduced an e-services system for its residents to view statements and account information
online [7]. The City of Cape Town Municipality allows its residents and businesses to view
and pay accounts, edit account details, retrieve electronic invoices and log customer queries
online [8]. Online self-service capabilities can only be introduced when a municipality has
adopted well-structured internal information systems to manage service delivery to
residents and businesses. While some municipalities have embraced the power of
information technology for the improvement of services to its citizens, a number of
municipalities have not adopted such systems, particularly those that serve rural areas. The
aim of this study is to develop a data model that can be used by all municipalities in South
Africa as the basis for automating their information systems for service delivery. The model
is developed from a combination of qualitative and quantitative methods. In this study
preliminary data was collected using: (1) a literature review, (2) observation of service
delivery processes at a selected municipality, and (3) online related resources from two
additional municipalities. The data was analysed and a generic conceptual municipality data
model was drawn. The data model was implemented in Oracle to create a test database for
future Semantic Web processing.
1.3 Background of Semantic Web and Ontology
After automating their information systems, municipalities can take advantage of Semantic
Web technologies to intelligently utilise the records in existing databases. Semantic Web is
a web of data from different sources that are linked together to create an integrated and
structured global data space [9, 10, 11]. It extracts information using its enabling
technologies and structures it in such a way that it can be understood by machines and
human beings [12]. Its enabling technologies are Ontology, Resource Description
Framework (RDF), Web Ontology Language (OWL) and many more. In fact, Semantic
Web technologies are applied in various domains (e-health, e-commerce, e-government,
etc.) to build a new generation of intelligent web applications. One of the main research
topics in Semantic Web is the semantic extraction of data stored in traditional relational
databases; different semantic web techniques and methods are being introduced in an
attempt to obtain a better way of presenting data stored in relational databases [13, 14, 15].
There is a need for new types of intelligent web applications that will utilise Semantic Web
technologies and tools to bring solutions that offer semantic reasoning, information search
and advanced query capabilities over the legacy databases of organizations. These
databases carry much organisational knowledge that remains highly under-utilised.
Municipalities in South African could benefit from Semantic Web technologies to better
their service delivery to citizens.
1.4 Problem Statement
South Africa is a developing country with many social and economic inequalities [4, 16].
These inequalities are also seen in ICT adoption in municipalities across the country;
consequently, there are developed, semi-developed and under-developed municipalities at
the local level of government [6, 7, 8, 20]. Generally municipalities in urban areas are able
to collect more revenues than those in rural areas. Therefore municipalities in urban areas

are obliged to automate their information systems to serve residents and businesses much
better. However, there are municipalities who have not automated their information systems
for better service delivery to the public as yet.
2. Objectives
The main objective of this study is to create a generic relational data model for
municipalities in South Africa. This relational data model is designed with the aim of
turning it into a database that will then be used to construct an Ontology using Semantic
Web technologies and tools.
3. Methodology
3.1 Data Collection and Analysis
Data was collected mainly from a survey of the literature and observations carried out at the
Emfuleni Municipality in Vanderbijlpark, South Africa. The Municipality’s Tariff and
Property Rates Policies for 2013/2014 financial year [2, 3] were downloaded from its public
website. The municipality’s monthly statements were analysed to obtain more information.
The Emfuleni Local Municipality provides services to developed towns such as
Vanderbijlpark and Vereeniging, and semi-developed townships including Sebokeng,
Bophelong, and Sharpville. Data was also collected from two other Municipalities in
Gauteng, namely, Lesedi Local Municipality in Heidelberg, and Ekurhuleni Metropolitan
Municipality in the East Rand Region. The Tariff and Property Rates Policies of these
municipalities were available on their websites [17, 18, 19]. The Lesedi Local Municipality
covers largely under-developed rural areas and two semi-developed small towns, namely,
Heidelberg and Devon [20], while the Ekurhuleni Metropolitan Municipality covers a
metropolitan area with developed cities and towns such as Germiston, Kempton Park,
Boksburg, and Bedfordview and townships such as Tembisa, Springs, Katlehong, Deverton
and the like. To build a generic data model, the business rules and service delivery
scenarios were derived from data collected at Emfuleni Local Municipality and compared
to the data from the Lesedi and Ekurhuleni Metropolitan Municipalities.
3.2 Relational Data Modelling
An Iterative Approach covered in [21, 22] was taken during the design of the relational data
model. The design had to go through a few iterations. The business rules and scenarios
derived above were able to produce the following potential entities after the first iteration:
Query, Administrator, Manager, Property Status, Group, Category, Query Type, Query
Status, Property, Customer Group, Customer, Account Status, Account, Account Billing,
Property Service, Service, Arrangement, Arrangement Status, Arrears, Payment Method
and Tariff. These entities were further searched in the data collected from the other two
municipalities and showed that these municipalities have compatible entities. Furthermore,
it was found that the entities: Services, Property, Property Type/Category, Tariff, and
Customer are common to all the three municipalities. After data comparison and further
analysis, the generic relational data model was drawn as in Figure 1.

AccountServices
PK ID
FK2 AccountID
FK1 PropertyServicesID
Date
Status
Consumption
Reading
Property
PK ID
FK1 PropertyTypeID
StandNo
StandAddress
RegisteredOwner
OwnerAddress
DateRegistered
MarketValue
Status
Customer
PK ID
Name
ID-RegistrationNo
PhysicalAddress
PostalAddress
Telephone
Cellphone
Email
Query
PK ID
FK1 CustomerID
Status
Type
DateEntered
DateClosed
Details
FK2 AttendedBy
Employee
PK ID
FK1 ManagerID
Name
Surname
Telephone
Cellphone
Email
Manager
PK ID
Name
Surname
Telephone
Cellphone
Email
Service
PK ID
Description
Type
PropertyService
PK ID
FK1 ServiceID
FK2 PropertyTypeID
Account
PK ID
FK2 CustomerID
FK1 PropertyID
DateOpened
Balance
Status
Arrangement
PK ID
FK1 AccountID
Date
Ballance
Installment
Status
Arrears
PK ID
FK1 AccountID
Date
Age
Amount
Payment
PK ID
FK1 AccountID
Date
Method
Amount
PropertyType
PK ID
Description
Tarrif
PK ID
FK1 ServiceID
FK2 PropertyTypeID
StartDate
EndDate
Price
Figure 1: Diagram of the Relational Data Model for South African Municipalities
3.3 Practical Implementations and Experiments
The resulting relational data model in Figure 1 was further implemented as a test database
in Microsoft SQL Server 2008. The test database was then loaded with sample data. To get
the same database structure in Oracle 11g, Oracle SQL Developer was utilised to migrate
the SQL Server Database to an Oracle Database.
4. Technology Description
4.1 Relational Data Model
Navanthe [23] described data modelling as a way of specifying and defining the structures
of data in either file systems or database management systems. Relational data modelling is
a simple form of data modelling which arranges data in the form of relations, tuples and
attributes [23]. In [24, 25], relational data model is stated as a concept that was introduced
by Codd during the inception and early development of relational database management
systems. In this study, entities derived from the data collection process are arranged in a
relational data model on which the relational test database is based.
4.2 Relational Database Management System (RDBMS) and Relational Database
Microsoft SQL Server and Oracle were the chosen RDBMS platforms to implement the test
relational database during experiments. These platforms were chosen because of their
widespread use. According to [26]; DB2, Oracle and Microsoft SQL Server are widely used
RDBMS platforms; these platforms dominate more than 85% of the market. This
demonstrates that Microsoft SQL Server and Oracle are amongst the RDBMS platforms
that are accepted by many organisations and enterprises.
In addition to being one of the most popular and highly used RDBMS platforms, Oracle
was chosen for Semantic Web reasons. According to [27, 28], Oracle and MySQL provide

specific views about the database metadata and are favoured during Semantic Web
development.
4.3 Ontology and Semantic Technologies
Ontology is an explicit specification of a conceptualisation which describes semantics of
data, providing a shared and common understanding of a domain [29]. Ontology is the most
important technology in Semantic Web; it is the backbone of every Semantic Web
application [12, 15, 30, 31]. RDF and OWL are languages used to represent ontologies [12].
5. Results
The relational data model shown in Figure 1 presents the resulting municipality data model
of this study. The data model was implemented into a functional test database using
Microsoft SQL Server and Oracle. Figure 2 shows a screenshot of a database in Oracle.
From the screenshot, it can be seen that the database has already been populated with
sample data. The relational database implemented in Figure 1 is going to be converted to an
Ontology which will allow semantic exploitation of its records. In fact, Semantic Web tools
like DataMaster and Protégé [32] are going to be applied to convert the Oracle database
into a well-structured Ontology (RDF/OWL). This Ontology can then be exploited
semantically to extract knowledge.
Figure 2: Screenshot of a Test Database in Oracle
6. Business Benefits
Municipalities in South Africa that have not automated their information systems can utilise
the relational data model presented in this study as an example to implement a central
functional municipality online system. Highly automated municipalities with functional
information systems can enhance their systems further by creating an Ontology that will

assist them in semantically extracting useful knowledge from their existing databases. This
study sets a tone for such a system enhancement process in municipalities.
7. Conclusions
7.1 Summary
This study produced a relational data model for municipalities in South Africa. Data was
collected using a literature review and observations based at the Emfuleni Local
Municipality in Vanderbijlpark, South Africa. For the purposes of generality, data was also
obtained from the Lesedi Local Municipality in Heidelberg and the Ekurhuleni
Metropolitan Municipality in the East Rand Region, Gauteng, South Africa. The data model
was then implemented into a test database using Microsoft SQL Server and Oracle as
RDBMS platforms of choice. The Oracle database is going to be converted into an
Ontology after which it will be semantically exploited. This model will contribute to a
better understanding and application of semantic web technologies for intelligent extraction
of useful information from legacy relational databases of municipalities in South Africa.
7.2 Further Work
The relational database developed in this study (figure 1 and Figure 2) is going to be used
to automatically build an Ontology which will allow semantic exploitation of
municipalities’ records. In fact, Semantic Web tools like DataMaster and Protégé [32] are
going to be applied to convert the Oracle database into a well-structured Ontology
(RDF/OWL). With the Ontology successfully extracted from the relational database, the
next phase of the study will be to develop a prototype front-end Semantic Web Application
that will be utilised to query and manipulate the constructed Ontology to provide automated
and intelligent service delivery to the public.
References
[1] Republic of South Africa, Constitution, 1996 Available at:
http://www.thehda.co.za/uploads/images/unpan005172.pdf [Accessed 10-12-2013]
[2] Emfuleni Local Municipality, Tariff Policy, 2013/2014 Financial Year. Available at:
http://www.emfuleni.gov.za/images/docs/policies/tariff_policy201314.pdf [Accessed 06.12.2013]
[3] Emfuleni Local Municipality, Property Rates Policy, 2013/2014 Financial Year. Available at:
http://www.emfuleni.gov.za/images/docs/policies/property_rates_policy201314.pdf [Accessed
06.12.2013]
[4] S.B., Koma “The state of Local Government in South Africa: Issues, trends and options”, Journal of
Public Administration, Vol. 45, No.1.1, pp. 111-120, 2010
[5] Republic of South Africa, Local Government Municipal Systems Act, Act 32 of 2000, 2000 Available
at: http://www.saflii.org.za/za/legis/num_act/lgmsa2000384.pdf [Accessed 10-12-2013]
[6] U.R., Averweg “Linking Public Service Delivery and Technology in a Municipal Information Society
in South Africa”, Information Technology in Developing Countries, A newsletter of the IFIP Working
Group 9.4, Vol. 22, No. 3, 2012
[7] City of Johannesburg, e-Services, http://eservices.joburg.org.za/joburg/eservices/#clkCntrl [Accessed
06.12.2013]
[8] City of Johannesburg, e-Services, http://eservices.joburg.org.za/joburg/eservices/#clkCntrl [Accessed
06.12.2013]
[9] T., Sadeh and J., Walker “Library Portals: Toward the Semantic Web”, New Library World, Vol. 104,
No.1184, pp. 11-19, 2003
[10] J., Zhang “Ontology and the Semantic Web”, In the Proceedings of the North American Symposium on
Knowledge Organisation, Toronto, Canada, 1-5 September, pp. 9-20, 2007
[11] N., Khozoie “Health Information Management on Semantic Web: (Semantic HIM)”, International
Journal of Web & Semantic Technology (IJWesT), Vol. 3, No.1, pp. 61-68, 2011
[12] G., Madhu, A., Govardhan, and T.V., Rajinikanth “Intelligent Semantic Web Search Engines: A Brief
Survey”, International Journal of Web & Semantic Technology (IJWesT), Vol. 2, No.1, pp. 34-42.,
2011

[13] S.H., Tirmizi, J., Sequeda and D., Miranker “Translating SQL Applications to the Semantic Web”, In
the Proceedings of the 19th International Conference, DEXA 2008, Turin, Italy, 1-5 September, pp.
450-464, 2008
[14] J.F., Sequeda, A., Marcelo and D.P., Miranker “On Directly Mapping Rational Databases to RDF and
OWL”, In the Proceedings of WWW 2012 – Session: Ontology Representation and Querying: RDF
and SPARQL, Lyon, France, 16-20 April, pp. 649-658, 2012
[15] D., Spanos, P., Stravrou, and N., Mitrou “Bringing Relational Databases into the Semantic Web: A
Survey”, Semantic Web Journal, Vol. 3, No.2, pp. 169-209., 2012
[16] Development Bank of South Africa, Delivering the democratic developmental state in South Africa,
Development Planning Division Working Paper, Number 9, 2009
[17] Lesedi Local Municipality, Property Rates Policy, 2012/2013 Financial Year. Available at:
http://www.lesedilm.gov.za/files/Final%20Rates%20Policy%202012-20123.pdf[Accessed 14.02.2014]
[18] Ekurhuleni Metropolitan Municipality, Rates Policy, Available at:
http://www.thepropertypartnership.co.za/pdf/EkurhuleniDraftRatesPolicyVersion8.pdf[Accessed
16.02.2014]
[19] Ekurhuleni Metropolitan Municipality, Tariff Schedules, Available at:
http://www.ekurhuleni.gov.za/484-tariff-schedules-for-idp-2013/file[Accessed 16.02.2014]
[20] Lesedi Local Municipality, About us, http://www.lesedilm.gov.za/about_us.php [Accessed 14.02.2014]
[21] V.R., Basili and A.J., Turner “Iterative Enhancement : A practical technique for Software
Development”, IEEE Transactions on Software Engineering, Vol. 1, No. 4, pp. 390-396, 1975
[22] D., Green and G., Ruhle “Software release planning: an evolutionary and iterative approach”,
Information and Software Technology, Vol. 46, No. 2004, pp. 243-253, 2004
[23] S.B., Navathe “Evolution of Data Modelling for Databases”, Communications of the ACM, Vol. 35,
No. 9, pp. 112-123, 1992
[24] S., Chakraborty “Codd’s Relational Data Model and Fuzzy Logic: A practical approach to find the
Computer Solution”, International Journal of Advanced Technology & Engineering Research
(IJATER), Vol. 2, No. 4, pp. 21-27, 2012.
[25] N., Mahmood, A., Burney, and K., Ahsan “A logical Temporal Relational Model”, International
Journal of Computer Science Issues, Vol. 7, No. 1, pp. 1-9.
[26] CY., Lin “Migrating to Relational Systems: Problems, Methods, and Strategies”, Contemporary
Management Research, Vol. 4, No. 4, pp. 369-380, 2008.
[27] S., Zhou, H., Ling, M., Han, and H., Zhang “Ontology Generator from Rational Database on Jena”,
Computer and Information Science Technology, Vol. 3, No.2, pp. 263-267, 2010
[28] N., Cullot, R., Ghawi, and K., Ytongnon “DB2OWL: A Tool for Automatic Database-to-Ontology
Mapping”, In: Michelangelo Ceci; Donato Malerba & Letizia Tanca, ed., ‘SEBD’, pp. 491-494, 2007
[29] M., Cristani, and R., Cuel “A Comprehensive Guideline for Building a Domain Ontology from
Scratch”, In the Proceedings of WWW 2012 – Session: Ontology Representation and Querying: RDF
and SPARQL, Graz, Austria, 30 June - 2 July, pp. 205-212, 2004.
[30] N., Imandi, and S.A.M., Rizvi “An Approach to OWL Concept Extraction and Integration across
Multiple Ontologies”, International Journal of Web & Semantic Technology (IJWesT), Vol. 3, No.3,
pp. 33-51., 2012
[31] A., Gali, C.X., Chen, K.T. Claypool, and R., Uceda-Sosa (2005) “From Ontology to Relational
Database”, Shan Wang et al. (Eds): Conceptual Modelling for Advanced Application Domains, Vol.
3289, pp. 278-289., 2005
[32] C., Nyulas, M., O’Connor and S., Tu “DataMaster – a Plug-in for Importing Schemas and Data from
Relational Databases into Protégé”, In the Proceedings of the 10th International Protégé Conference,
Budapest, Hungary, 15-18 July, 2007

ISTAfrica_Paper_ref_112_6583

More Related Content

What's hot (20)

Similar to ISTAfrica_Paper_ref_112_6583 (20)

ISTAfrica_Paper_ref_112_6583