Data mining for digital mobile telecommunication network's quality of service performance measuremen (original) (raw)

Mining Operational Data for Improving GSM Network Performance

International Conference on Fuzzy Systems and …

Operation and maintenance system (OMS) is an important part of network management in GSM networks. A large amount of operational data is generated daily in a mobile communication system, which contains hidden valuable information about the system behavior. The knowledge discovered from mining on such data can be used to analyze network performance and aid in long term planning. In this paper, we devise a solution procedure for mining correlated patterns from the operational data of a GSM system that come in a variety of formats. The results will be exploited for finding the critical factors that will affect the network quality and performance. Furthermore, it is believed that when a set of threshold values (that are used in OMS) are predicted in real-time by using a fuzzy classifier system from the rules generated from the past cases, the grade-of-service could be improved while maintaining a minimum number of cells.

Data Mining for Resource Planning and QoS Supports in GSM Networks

2011

Abstract Applications that run on mobile phones shaped a trendy lifestyle for many users nowadays. This led to a significant growth in the proportion of data traffic, relative to voice traffic, to be delivered in the mobile phone network such as GSM. Traditionally the underlying radio resources in GSM networks for data and voice traffic were allocated by some predefined traffic policy which was manually configured. The allocation may not be most accurate for the fact that demands for data traffic fluctuate largely and temporally.

Data Mining in Telecommunication

Telecommunication is one of the first industry to adopt Data mining technology. Generally these companies have many customers. So companies save this data and hence there is generation of tremendous amount of data. This data includes call-detail data, customer data, network data. To handle this large amount of data and to discover useful information from this data the automatic or semiautomatic method should be used as it simplifies the work. And the Data Mining fits for this criteria. Therefore use of Data Mining technology is must for telecommunication companies. This paper describes how data mining and its applications helps Telecommunication industry in various aspects.

Data Mining in Telecommunication Industry

Telecommunication companies today are operating in highly competitive and challenging environment. Vast volume of data is generated from various operational systems and these are used for solving many business problems that required urgent handling. These data include call detail data, customer data and network data. Data Mining methods and business intelligence technology are widely used for handling the business problems in this industry. The goal of this paper is to provide a broad review of data mining concepts.

DATA MINING IN TELECOMMUNICATIONS

Telecommunication companies generate 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. This chapter describes how data mining can be used to uncover useful information buried within these data sets. Several data mining applications are described and together they demonstrate that data mining can be used to identify telecommunication fraud, improve marketing effectiveness, and identify network faults.

Analysis of Mobile Service Providers Performance Using Naive Bayes Data Mining Technique

International Journal of Electrical and Computer Engineering (IJECE), 2018

Recently, the mobile service providers have been growing rapidly in Malaysia. In this paper, we propose analytical method to find best telecommunication provider by visualizing their performance among telecommunication service providers in Malaysia, i.e. TM Berhad, Celcom, Maxis, U-Mobile, etc. This paperuses data mining technique to evaluate the performanceof telecommunication service providers using their customers feedback from Twitter Inc. It demonstrates on how the system could process and then interpret the big data into a simple graph or visualization format. In addition, build a computerized tool and recommend data analytic model based on the collected result. From prepping the data for pre-processing until conducting analysis, this project is focusing on the process of data science itself where Cross Industry Standard Process for Data Mining (CRISP-DM) methodology will be used as a reference. The analysis was developed by using R language and R Studio packages. From the result, it shows that Telco 4 is the best as it received highest positive scores from the tweet data. In contrast, Telco 3 should improve their performance as having less positive feedback from their customers via tweet data. This project bring insights of how the telecommunication industries can analyze tweet data from their customers. Malaysia telecommunication industry will get the benefit by improving their customer satisfaction and business growth. Besides, it will give the awareness to the telecommunication user of updated review from other users.

A Complete Data Mining process to Manage the QoS of ADSL Services

2012

In this paper we explore the interest of computational intelligence tools in the management of the Quality of service (QOS) for ADSL lines. The paper presents the platform and the mechanism used to monitoring the quality of service of the Orange ADSL network in France in the context of the availability of the VoIP services. In particular, this platform allows the detection and the classification of the unstable lines in the network. The interpretation of results given by the classification process allows the discovery of a knowledge used to improve the process which labels the lines (stable/unstable) and to prevent inefficient supervision of the network.