| On the quantification of e-business capacity |
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Electronic Commerce
archive
Proceedings of the 3rd ACM conference on Electronic Commerce
table of contents
Tampa, Florida, USA
Pages: 235 - 244
Year of Publication: 2001
ISBN:1-58113-387-1
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Downloads (6 Weeks): 11, Downloads (12 Months): 56, Citation Count: 3
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ABSTRACT
In order for current e-Businesses to mature from hastily assembled systems and applications, formal processes must be put in place for planning and budgeting, pricing and costing, and for establishing quality of service and service--level assurances. There are many challenges that e-Businesses face in formalizing these processes. The most important problem is to bridge the semantic disconnect between business objectives and the information system performance objectives. Next, the characterization of the e-Business infrastructure is extremely complex, given the variety of applications and system configurations at a web site and the traffic it receives. Finally, e-Businesses need to associate and apply traditional economic factors, such as depreciation and usage to applications, operating systems, and databases. In this paper, we propose an approach for defining and quantifying effective e-Business capacity that allows us to translate quality of service objectives into the number of users that a web site can support. This approach is based on inducing online models using machine learning and statistical pattern recognition techniques. As a consequence, the approach is flexible: it adapts to any site configuration and environment. The concept of e-Business capacity allows us to naturally answer planning and operational questions about the information system infrastructure needed to support the e-Business. The questions range from indicating which performance measures in the system are "important" to simulating "if-then" scenarios
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 3
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David A. Bacigalupo , Stephen A. Jarvis , Ligang He , Daniel P. Spooner , Donna N. Dillenberger , Graham R. Nudd, An Investigation into the Application of Different Performance Prediction Methods to Distributed Enterprise Applications, The Journal of Supercomputing, v.34 n.2, p.93-111, November 2005
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