Proposal of using business information in a QoS mechanism for the e-commerce Web server

• Autorzy: Suchacka G.

Warianty tytułu

PL

Propozycja wykorzystania informacji biznesowych w mechanizmie jakości usług dla serwera e-commerce

• Języki publikacji: EN

Abstrakty

EN

Due to very negative and long-term consequences of a low quality of service (QoS) for e-business, a number of QoS mechanisms for Web servers were proposed. As a continuation of this research trend, the paper proposes a new way of using business information in an admission control and scheduling scheme for the e-commerce server aiming at the integration of the server system efficiency with e-business profitability.

PL

Tematyka pracy dotyczy problemu jakości usług ośrodków webowych. Zaproponowano nowatorski sposób wykorzystania informacji biznesowych w metodzie kontroli przyjęć i szeregowania żądań dla serwisu e-commerce. Celem metody jest połączenie aspektu wydajności serwisu webowego oraz rentowności elektronicznego biznesu.

Słowa kluczowe

EN

web server; quality of Web service; QoWS; e-commerce; RFM

PL

Web serwer; Quality of Web Service; QoWS; e-commerce; RFM

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Studia Informatica
• Rocznik: 2009
• Tom: Vol. 30, nr 4
• Strony: 81--95
• Opis fizyczny: Bibliogr. 14 poz.

Twórcy

autor

Suchacka G.

• Chair of Computer Science, Opole University of Technology, ul. K. Sosnowskiego 31, 45-272 Opole,

Bibliografia

• 1. Buystream, E-Metric Research Group: Measure Twice, Cut Once - Metrics For Online Retailers, http://www.techexchange.com/thelibrary/online_retail_metrics.html.
• 2. Chen M.-C., Chiu A.-L., Chang H.-H.: Mining Changes in Customer Behavior in Retail Marketing. Expert Systems With Applications, Vol. 28, Issue 4, May 2005, p. 773-781.
• 3. Hughes A., M.: Making Your Database Pay Off - Using Recency, Frequency and Monetary Analysis. Database Marketing Insititute, January 2001.
• 4. Kihl M., Widell N.: Admission Control Schemes Guaranteeing Customer QoS in Commercial Web Sites. IFIP Conference NetCon’02, Vol. 235, 2002, p. 305-316.
• 5. Liu D., R., Shih Y.-Y.: Integrating AHP and Data Mining for Product Recommendation based on Customer Lifetime Value. Information and Management, Vol. 42, Issue 3, March 2005, p.387-400.
• 6. Menasce D. A., Almeida V. A. F., Fonseca R., Mendes M. A.: Business-Oriented Resource Management Policies for E-Commerce Servers. Performance Evaluation, Vol. 42, Issue 2-3, September 2000, p. 223-239.
• 7. Miglautsch J. R.: Thoughts on RFM Scoring. ISSM Electronic Journal, Issue 27, 2001.
• 8. Nielsen J.: Why people shop on the Web, February 1999, updated: April 2002, http://www.useit.com/alertbox/990207.html; access date: March 12, 2008.
• 9. Novo J.: Turning Customer Data into Profits with a Spreadsheet. The Guide to Maximizing Customer Marketing ROI, Booklocker.com, 3rd edition, 2004.
• 10. Pecaut D. K., Silverstein M. J., Stanger P.: Winning the Online Consumer: Insights into Online Consumer Behavior. Boston Consulting Group, March 2000, http://www.bcg.com.
• 11. Schwetman H.: CSIM19: A powerful tool for building system models. In Proc. of the 2001 Winter Simulation Conference, 2001.
• 12. Silverstein M., Stanger P., Abdelmessih N.: Winning the Online Consumer 2.0. Converting Traffic into Profitable Relationship. A report by BCG, February 2001.
• 13. Totok A., Karamcheti V.: Improving Performance of Internet Services Through Reward-Driven Request Prioritization. IWQoS’06, June 2006, p. 60-71.
• 14. Zhou X., Wei J., Xu C.-Z.: Resource Allocation for Session-Based Two-Dimensional Service Differentiation on e-Commerce Servers. IEEE TPDS, 17(8), 2006, p. 838-850.