A Proposal for the Management of Mobile Network's Quality of Service (QoS) using Data Mining Methods

2007 ◽  
Author(s):  
MPS Bhatia ◽  
Harender Singh ◽  
Naresh Kumar
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Mining Methods ◽  
Using Data

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.

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.

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.

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.

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.

scholarly journals Discretization of data using Boolean transformations and information theory based evaluation criteria

2015 ◽  
Vol 63 (4) ◽  
pp. 923-932 ◽  
Author(s):  
C. Jankowski ◽  
D. Reda ◽  
M. Mańkowski ◽  
G. Borowik
Keyword(s):  
Data Mining ◽  
Information Theory ◽  
Heuristic Algorithms ◽  
Evaluation Criteria ◽  
Decision Table ◽  
Compression Method ◽  
Mining Methods ◽  
Using Data ◽  
Numeric Data

Abstract Discretization is one of the most important parts of decision table preprocessing. Transforming continuous values of attributes into discrete intervals influences further analysis using data mining methods. In particular, the accuracy of generated predictions is highly dependent on the quality of discretization. The paper contains a description of three new heuristic algorithms for discretization of numeric data, based on Boolean reasoning. Additionally, an entropy-based evaluation of discretization is introduced to compare the results of the proposed algorithms with the results of leading university software for data analysis. Considering the discretization as a data compression method, the average compression ratio achieved for databases examined in the paper is 8.02 while maintaining the consistency of databases at 100%.

Data Mining zur Nacharbeitsdauerprognose*/Data Mining-based forecasting of rework duration - Predictive rework control and work process optimization in the automotive assembly

2017 ◽  
Vol 107 (10) ◽  
pp. 773-778
Author(s):  
S. Krzoska ◽  
M. Eickelmann ◽  
J. Schmitt ◽  
J. Prof. Deuse
Keyword(s):  
Data Mining ◽  
Process Optimization ◽  
Quality Data ◽  
Operating Process ◽  
Automotive Assembly ◽  
Related Quality ◽  
Mining Methods ◽  
Product And Process ◽  
Using Data ◽  
Manufacturing Execution

Der Fachbeitrag zeigt am Beispiel der Nacharbeitssteuerung und Arbeitsprozessoptimierung in der Automobilmontage, wie produkt- und prozessbezogene Qualitätsdaten durch den Einsatz von Data Mining-Methoden analysiert sowie effizient genutzt werden können. Dazu wurden Daten aus Manufacturing-Execution-Systemen (MES) mithilfe von Regressionsbäumen zur Entwicklung einer fahrzeugspezifischen Nacharbeitsdauerprognose ausgewertet. Das grundlegende Data Mining-Konzept sowie die Pilotierungsergebnisse werden nachfolgend dargestellt.   The article shows at the example of rework control and operating process optimization in the car assembly how recorded product- and process-related quality data can be analyzed and used efficiently by using Data Mining-methods. With data from MES-systems regression trees were built for a vehicle-specific rework duration forecast. The basic concept and validation results will be presented below.

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