Enabling customer relationship management in ISP services through mining usage patterns

Li, Sheng-Tun; Shue, Li–Yen; Lee, Shu-Fen

doi:10.1016/j.eswa.2005.07.016

Cited by 26 publications

(12 citation statements)

References 16 publications

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“…In quality management we found some applications of data mining such as use of data mining to improve personnel selection (Chien & Chen 2008) , in estimating the impact of project management policies on software quality (Moreno García et al 2008) and to enable customer relationship in ISP services through mining usage patterns (Li et al 2006). …”

Section: Data Mining In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Data Mining In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Simultaneously, the higher the service quality, the more new and existing customers could be attracted and retained, and even lure customers away from competitors (Babakus et al, 2004;Petruzzellis et al, 2006). Therefore, service quality is crucial for attracting new customers and, more significantly, for retaining existing ones (Li et al, 2006). This issue has been one of the most discussed matters in the marketing literature, and is seen as a vital element in management strategies to achieve success or survive in a competitive environment (Martínez and Martínez, 2007).…”

Section: Service Qualitymentioning

confidence: 98%

Correlations between external knowledge and the knowledge chain as impacting service quality

Tseng

2012

Journal of Retailing and Consumer Services

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“…Referring to [2], [3], the research objectives were customer service management [2] and support of the development of service management strategy [3]. They use network data, whereas most other researchers paid more attention to the required bandwidth to access the Internet.…”

Section: Previous Workmentioning

confidence: 99%

Characterizing corporate network traffic beyond bandwidth

Tanutama

2014

2014 2nd International Conference on Information and Communication Technology (ICoICT)

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Companies increasingly rely on Internet for effective and efficient business communication. As Information Technology (IT) provides the infrastructure backbone for business activities, a corporate network connects the company to Internet and enables it to perform its activities globally. The corporate carries data packets generated by the users' while performing their business tasks. Traditionally, the infrastructure operations mainly maintain the data carrying capacity and network devices performance. The research provides a simple method of mapping the business activity reflected by the network data. Modified Attribute Oriented Induction (AOI) approach mines the network data for mapping corporate users' activities. A frequency count was taken for each attributes of interest toshow what happened in the network. The collected data were samples taken within a certain sampling period. based on the frequency count, we define four novel corporate network characteristics: Network Infrastructure characteristics, User Characteristics, Accessibility Characteristics, and Activity Characteristics. It can be concluded that the method could provide the management a general overview of the usage of its infrastructure and lead to efficient, effective and secure Information and Communication Technology (ICT) infrastructure.

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Enabling customer relationship management in ISP services through mining usage patterns

Cited by 26 publications

References 16 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Correlations between external knowledge and the knowledge chain as impacting service quality

Characterizing corporate network traffic beyond bandwidth

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