TABLE OF CONTENTS
| Title Page | …………………………………………………………… | i |
| Approval Page | ………………………………………………………….. | ii |
| Declaration | ……………………………………………………………. | iii |
| Dedication | …………………………………………………………….. | iv |
| Acknowledgement ………………………………………………………. | v | |
| Abstract | ……………………………………………………………… | vi |
Table of Contents …………………………………………………….. vii – ix
CHAPTER ONE – INTRODUCTION
- Background of the Study ………………………………………… 1-3
- Statement of General Problem …………………………………… 3-4
- Objective of the Study ………………………………………… 4
1.6 Definition of Terms ……………………………………………… 5
CHAPTER TWO – REVIEW OF RELATED LITERATURE
2.0 Introduction ………………………………………………………. 6
2.1.1 Call detail data ……………………………………………………. 7-9
2.1.2 Network data ………………………………………………………. 9
2.1.3 Customer data ……………………………………………………… 10
- Data Mining Applications ………………………………………….. 10
- Marketing/customer relationship management ……………………… 10-12
2.2.2 Customer churn ……………………………………………………… 12-13
2.3 Network unavailability ………………………………………………. 13
2.4 Tariffs ……………………………………………………………….. 13-14
2.5 Subscriber churning …………………………………………………… 15
2.6 Fraud detection ………………………………………………………. 15-16
2.7 Network fault isolation ………………………………………………. 16-18
2.8 Empirical review ……………………………………………………… 18-20
CHAPTER THREE – SYSTEM ANALYSIS AND DESIGN
3.1 Introduction ……………………………………………………… 21
3.2 Data preparation ………………………………………………. 21-22
3.3 System Design ……………………………………………………. 22-23
3.4 Monitoring and administration …………………………………… 23
3.5 ETLR ……………………………………………………………… 23-25
3.6 Data warehouse …………………………………………………… 26-27
3.7 Method of Data analysis …………………………………………… 27
3.8 Knowledge discovery ………………………………………………. 27-28
CHAPTER FOUR – IMPLEMENTATION, TESTING AND INTEGRATION
4.0 Choice of developing tools ………………………………….. 29
4.1 System requirement ………………………………………….. 29
- Software requirement………………………………………………………………. 29
- Hardware requirements…………………………………………………………….. 29
- System results………………………………………………………………………… 31-35
CHAPTER FIVE – SUMMARY, CONCLUSION AND RECOMMENDATION
- Summary of findings………………………………………………………….. 36
- Conclusion……………………………………………………………………….. 36
- Recommendations…………………………………………………………….. 36
References Appendix A
CHAPTER ONE
INTRODUCTION
A decision support system is a system with a graphic interface that allows interactive analysis of the different scenarios presented. The system contains a set of mathematical programming models and has the capability to dynamically construct and solve instances of those models. It also provides data presentation and reports. The system is an integrated, user friendly and a powerful tool to making planning studies by firms developing cable network systems in the telecommunication market.
1.1 BACKGROUND TO THE STUDY
(Han et al, 2012) The telecommunications industry generates and stores a tremendous amount of data. These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which describes the telecommunication customers (Roset et al, 2012). The amount of data is so great that manual analysis of the data is difficult, if not impossible. The need to handle such large volumes of data led to the development of knowledge-based expert systems. These automated systems perform important functions such as identifying fraudulent phone calls and identifying network faults. The problem with this approach is that it is time consuming to obtain the knowledge from human experts and, in many cases, the experts do not have the requisite knowledge. The advent of decision support system promise solutions to these problems and for this reason the telecommunications industry was an early adopter of decision support system.
Telecommunication data have several interesting issues for data mining. The first concerns scale, since telecommunication databases may contain billions of records and are amongst the
largest in the world. A second issue is that the raw data is often not suitable for data mining. For example, both call detail and network data are time-series data that represent individual events. Before this data can be effectively mined, useful summary features must be identified and then the data must be summarized using these features, because many data mining applications in the telecommunications industry involve predicting very rare events, such as the failure of a network element or an instance of telephone fraud, rarity is another issue that must be dealt with. The fourth and final data mining issue concerns real-time performance because many data mining applications, such as fraud detection, require that any learned model/rules be applied in real-time (Ezawa& Norton, 2015). Several techniques being applied is tackling all these issues in telecommunication companies.
Telecommunication networks are extremely complex configurations of equipment, comprised of thousands of interconnected components. Each network element is capable of generating error and status messages, which leads to a tremendous amount of network data. This data must be stored and analyzed in order to support network management functions, such as fault isolation/detection. This data will include a time stamp, a string that uniquely identifies the hardware or software component generating the message and a code that explains why the message is being generated. For example, such a message might indicate that “controller 1 experienced a loss of power for 40 seconds starting at 09:03am on Tuesday, June 13.”
Due to the large number of network messages generated, technicians cannot possibly handle every message. For this reason expert systems have been developed to automatically analyze these messages and take appropriate action, only involving a technician when a problem cannot be automatically resolved. This study is focused on AIRTEL NIGERIA.
Formally known as Celtel Nigeria, the company was established in 2000, by a group of institution and private investors as well as three state governments.
DECISION SUPPORT SYSTEM FOR TELECOMMUNICATION COMPANIES IN NIGERIA (A CASE STUDY OF AIRTEL NIGERIA)
