Analysis of Data Mining Techniques of Telecommunication Companies in Nigeria: a Case Study of Mtn Nigeria

Chapter One Of Analysis of Data Mining Techniques of Telecommunication Companies in Nigeria: a Case Study of Mtn Nigeria

INTRODUCTION

BACKGROUND TO THE STUDY

The telecommunications industry generates and stores a tremendous amount of data (Han et al, 2002). 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, 1999). 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 performed 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 (the “knowledge acquisition bottleneck”) and, in many cases, the experts do not have the requisite knowledge. The advent of data mining technology promised solutions to these problems and for this reason the telecommunications industry was an early adopter of data mining technology (Roset et al, 1999).
Telecommunication data pose 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, 1995). Several techniques has also been 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. This data will minimally include a timestamp, 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 7 experienced a loss of power for 30 seconds starting at 10:03 pm on Monday, May 12.”

Due to the enormous 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 (Weiss, Ros & Singhal, 1998). This study is focused on MTN Nigeria.
MTN Nigeria is part of the MTN Group, Africa’s leading cellular telecommunications company. On May 16, 2001, MTN became the first GSM network to make a call following the globally lauded Nigerian GSM auction conducted by the Nigerian Communications Commission earlier in the year. Thereafter the company launched full commercial operations beginning with Lagos, Abuja and Port Harcourt. MTN paid $285m for one of four GSM licenses in Nigeria in January 2001. To date, in excess of US$1.8 billion has been invested building mobile telecommunications infrastructure in Nigeria.

Since launch in August 2001, MTN has steadily deployed its services across Nigeria. It now provides services in 223 cities and towns, more than 10,000 villages and communities and a growing number of highways across the country, spanning the 36 states of the Nigeria and the Federal Capital Territory, Abuja. Many of these villages and communities are being connected to the world of telecommunications for the first time ever.

STATEMENT OF THE PROBLEM
Fraud is a serious problem for telecommunication companies, leading to billions of dollars in lost revenue each year. Fraud can be divided into two categories: subscription fraud and superimposition fraud. Subscription fraud occurs when a customer opens an account with the intention of never paying for the account charges. Superimposition fraud involves a legitimate account with some legitimate activity, but also includes some “superimposed” illegitimate activity by a person other than the account holder. Superimposition fraud poses a bigger problem for the telecommunications industry and for this reason data mining technique is used for identifying this type of fraud. These applications should ideally operate in real-time using the call detail records and, once fraud is detected or suspected, should trigger some action. This action may be to immediately block the call and/or deactivate the account, or may involve opening an investigation, which will result in a call to the customer to verify the legitimacy of the account activity. However, this study will examine various data mining techniques of telecommunication companies in Nigeria.

OBJECTIVES OF THE STUDY
The following are the objectives of this study:

1. To provide an overview on data mining.
2. To examine the various data mining techniques of telecommunication companies in Nigeria
3. To identify the challenges of data mining faced by telecommunication companies in Nigeria

RESEARCH QUESTIONS

1. What is data mining?
2. What are the various data mining techniques of telecommunication companies in Nigeria?
3. What are the challenges of data mining faced by telecommunication companies in Nigeria?

SIGNIFICANCE OF THE STUDY
The following are the significance of this study:

1. The outcome of this study will educate on data mining techniques of telecommunication companies in Nigeria, the data mining applications and how they can be used in fraud detection.
2. This research will be a contribution to the body of literature in the area of the effect of personality trait on student’s academic performance, thereby constituting the empirical literature for future research in the subject area.

SCOPE/LIMITATIONS OF THE STUDY
This study will cover various data mining techniques used by telecommunication companies in Nigeria.
LIMITATION OF STUDY
Financial constraint– Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).
Time constraint– The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work.

REFERENCES
Weiss, G. M., Ros, J, Singhal, A. ANSWER: Network monitoring using object-oriented rule. Proceedings of the Tenth Conference on Innovative Applications of Artificial Intelligence; 1087-1093. AAAI Press, Menlo Park, CA, 1998.
Ezawa, K., Norton, S. Knowledge discovery in telecommunication services data using Bayesian network models. Proceedings of the First International Conference on Knowledge Discovery and Data Mining; 1995 August 20-21. Montreal Canada. AAAI Press: Menlo Park, CA, 1995.
Han, J., Altman, R. B., Kumar, V., Mannila, H., Pregibon, D. Emerging scientific applications in data mining. Communications of the ACM 2002; 45(8): 54-58
Roset, S., Murad, U., Neumann, E., Idan, Y., Pinkas, G. Discovery of fraud rules for telecommunications—challenges and solutions. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 409-413, San Diego CA. New York: ACM Press, 1999.