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Analyzing Customer Behavior Model Graph (CBMG) using Markov Chains

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2 Author(s)
Kaszo Mark ; Budapest University of Technology and Economics, Hungary, Department of Automation and Applied Informatics, Goldmann Gyöorgy tér 3. IV. em., Budapest, H-1111, Hungary. Tel: 463-2870, Fax: 463-2871, mkaszo@aut.bme.hu ; Legany Csaba

Performance is one of the main challenges in designing an e-commerce or e-business application. This article proposes a new method for estimating one important parameter of system workload, the average visit length. In order to characterize system workload, common techniques, like Customer behavior model graphs (CBMG) or Markov chains can be applied. This paper introduces a new method for transforming CBMG graphs to stable Markov chains. It is proven by measurement results that the average visit length converge to the stationary distributions of the Markov chain representations. A new method for calculating the average recurrent time as the average session length is also presented.

Published in:

2007 11th International Conference on Intelligent Engineering Systems

Date of Conference:

June 29 2007-July 2 2007