Optimizing and Managing Digital Telecommunication Systems Using Data Mining and Knowledge Discovery Approaches

2011 ◽  
pp. 2360-2379
Author(s):  
Adnan I. Al Rabea ◽  
Ibrahiem M. M. El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Knowledge Discovery ◽  
Self Organizing Map ◽  
Performance Measurements ◽  
Operating Personnel ◽  
Telecommunication Systems ◽  
Original Measurement ◽  
Using Data

This chapter is interested in discussing and reporting how one can be benefited by using Data Mining and Knowledge Discovery techniques in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which is predicated by using the data mining techniques, decision tree, association rules and neural networks. Digital telecommunication networks are highly complex systems and thus their planning, management and optimization are challenging tasks. The user expectations constitute the Quality of Service (QoS). To gain a competitive edge on other operators, the operating personnel have to measure the network in terms of QoS. In current times, there are three data mining methods applied to actual GSM network performance measurements, in which the methods were chosen to help the operating staff to find the essential information in network quality performance measurements. The results of Pekko (2004) show that the analyst can make good use of Rough Sets and Classification and Regression Trees (CART), because their information can be expressed in plain language rules that preserve the variable names of the original measurement. In addition, the CART and the Self-Organizing Map (SOM) provide effective visual means for interpreting the data set.

Optimizing and Managing Digital Telecommunication Systems Using Data Mining and Knowledge Discovery Approaches

2010 ◽  
pp. 66-84
Author(s):  
Adnan I. Al Rabea ◽  
Ibrahiem M. M. El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Knowledge Discovery ◽  
Self Organizing Map ◽  
Performance Measurements ◽  
Operating Personnel ◽  
Telecommunication Systems ◽  
Original Measurement ◽  
Using Data

This chapter is interested in discussing and reporting how one can be benefited by using Data Mining and Knowledge Discovery techniques in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which is predicated by using the data mining techniques, decision tree, association rules and neural networks. Digital telecommunication networks are highly complex systems and thus their planning, management and optimization are challenging tasks. The user expectations constitute the Quality of Service (QoS). To gain a competitive edge on other operators, the operating personnel have to measure the network in terms of QoS. In current times, there are three data mining methods applied to actual GSM network performance measurements, in which the methods were chosen to help the operating staff to find the essential information in network quality performance measurements. The results of Pekko (2004) show that the analyst can make good use of Rough Sets and Classification and Regression Trees (CART), because their information can be expressed in plain language rules that preserve the variable names of the original measurement. In addition, the CART and the Self-Organizing Map (SOM) provide effective visual means for interpreting the data set.

Role of Data Mining and Knowledge Discovery in Managing Telecommunication Systems

2010 ◽  
pp. 14-29
Author(s):  
Ibrahiem Mahmoud Mohamed El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Expert Systems ◽  
Telecommunications Industry ◽  
Data Mining Techniques ◽  
Telecommunication Systems ◽  
The Status ◽  
Using Data

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

Role of Data Mining and Knowledge Discovery in Managing Telecommunication Systems

Data Mining ◽  
2013 ◽  
pp. 1591-1606
Author(s):  
Ibrahiem Mahmoud Mohamed El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Expert Systems ◽  
Telecommunications Industry ◽  
Data Mining Techniques ◽  
Telecommunication Systems ◽  
The Status ◽  
Using Data

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

Path Mining in Web Processes Using Profiles

2011 ◽  
pp. 896-901 ◽  
Author(s):  
Jorge Cardoso
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Business Process ◽  
Management System ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Data Mining Techniques ◽  
Using Data

Business process management systems (BPMSs) (Smith & Fingar, 2003) provide a fundamental infrastructure to define and manage business processes, Web processes, and workflows. When Web processes and workflows are installed and executed, the management system generates data describing the activities being carried out and is stored in a log. This log of data can be used to discover and extract knowledge about the execution of processes. One piece of important and useful information that can be discovered is related to the prediction of the path that will be followed during the execution of a process. I call this type of discovery path mining. Path mining is vital to algorithms that estimate the quality of service of a process, because they require the prediction of paths. In this work, I present and describe how process path mining can be achieved by using data-mining techniques.

scholarly journals Using Data Mining to Analyze Dwell Time and Nonstop Running Time in Road-Based Mass Transit Systems

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1217
Author(s):  
Teresa Cristóbal ◽  
Gabino Padrón ◽  
Alexis Quesada ◽  
Francisco Alayón ◽  
Gabriel de Blasio ◽  
...  
Keyword(s):  
Data Mining ◽  
Travel Time ◽  
Dwell Time ◽  
Mass Transit ◽  
Transit Systems ◽  
Running Time ◽  
Bus Stop ◽  
Transport Line ◽  
Using Data

Travel Time plays a key role in the quality of service in road-based mass transit systems. In this type of mass transit systems, travel time of a public transport line is the sum of the dwell time at each bus stop and the nonstop running time between pair of consecutives bus stops of the line. The aim of the methodology presented in this paper is to obtain the behavior patterns of these times. Knowing these patterns, it would be possible to reduce travel time or its variability to make more reliable travel time predictions. To achieve this goal, the methodology uses data related to check-in and check-out movements of the passengers and vehicles GPS positions, processing this data by Data Mining techniques. To illustrate the validity of the proposal, the results obtained in a case of use in presented.

Suspicious Behavior Detection in Debit Card Transactions using Data Mining

2015 ◽  
Vol 28 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Ehsan Saghehei ◽  
Azizollah Memariani
Keyword(s):  
Data Mining ◽  
Hybrid Model ◽  
Real Life ◽  
Final Analysis ◽  
Self Organizing Map ◽  
High Ability ◽  
Detection Problem ◽  
Behavior Detection ◽  
Using Data ◽  
Debit Cards

The approach used in this paper is an implementation of a data mining process against real-life transactions of debit cards with the aim of detecting suspicious behavior. The framework designed for this purpose has been obtained through merging supervised and unsupervised models. First, due to unlabeled data, Twostep and Self-Organizing Map algorithms have been used in clustering the transactions. A C5.0 classification algorithm has been applied to evaluate supervised models and also to detect suspicious behaviors. An innovative plan has been designed to evaluate hybrid models and select the most appropriate model for the solution of the fraud detection problem. The evaluation of the models and the final analysis of the data took place in four stages. The appropriate hybrid model was selected from among 16 models. The results show a high ability of selected model in detecting suspicious behavior in transactions involving debit cards.

Data-Mining Techniques for an Analysis Of Non-Conventional Methodologies

2010 ◽  
pp. 82-91
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Data Mining ◽  
Alternative Medicine ◽  
Knowledge Discovery ◽  
Western Medicine ◽  
Musculoskeletal Complaints ◽  
Scientific Methods ◽  
Using Data ◽  
Data Elements ◽  
Discovery Algorithms

Some common methodologies in our everyday life are not based on modern scientific knowledge but rather a set of experiences that have established themselves through years of practice. As a good example, there are many forms of alternative medicine, quite popular, however difficult to comprehend by conventional western medicine. The diagnostic and therapeutic methodologies are very different and sometimes unique, compared to that of western medicine. How can we verify and analyze such methodologies through modern scientific methods? We present a case study where data-mining was able to fill this gap and provide us with many tools for investigation. Osteopathy is a popular alternative medicine methodology to treat musculoskeletal complaints in Japan. Using data-mining methodologies, we could overcome some of the analytical problems in an investigation. We studied diagnostic records from a very popular osteopathy clinic in Osaka, Japan that included over 30,000 patient visits over 6 years of practice. The data consists of some careful measurements of tissue electro-conductivity differences at 5 anatomical positions. Data mining and knowledge discovery algorithms were applied to search for meaningful associations within the patient data elements recorded. This study helped us scientifically investigate the diagnostic methodology adopted by the osteopath.

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