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Mining association rules for admission control and service differentiation in e-commerce applications
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Xue, James and Jarvis, Stephen A. (2018) Mining association rules for admission control and service differentiation in e-commerce applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery . e1241. doi:10.1002/widm.1241 (In Press)
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WRAP-mining-association-rules-admission-control-service-differentiation-e-commerce-applications-Jarvis-2018.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-Noncommercial 4.0. Download (1528Kb) | Preview |
Official URL: http://dx.doi.org/10.1002/widm.1241
Abstract
Workload demands in e-commerce applications are very dynamic in nature, therefore it is essential for internet service providers to manage server resources effectively to maximize total revenue in server overloading situations. In this paper, a data mining technique is applied to a typical e-commerce application model for identification of composite association rules that capture user navigation patterns. Two algorithms are then developed based on the derived rules for admission control, service differentiation, and priority scheduling. Our approach takes the following aspects into consideration: (a) only final purchase requests result in company revenue; (b) any other request can potentially lead to final purchase, depending upon the likelihood of the navigation sequence that starts from current request and leads to final purchase; (c) service differentiation and priority assignment are based on aggregated confidence and average support of the composite association rules. As identification of composite association rules and computation of confidence and support of the rules can be pre-computed offline, the proposed approach incurs minimum performance overheads. The evaluation results suggest that the proposed approach is effective in terms of request management for revenue maximization.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
| Divisions: | Faculty of Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Data mining, Web usage mining, Electronic commerce -- Computer network resources, Association rule mining, Cloud computing, Electronic digital computers -- Workload | ||||||
| Journal or Publication Title: | Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery | ||||||
| Publisher: | John Wiley & Sons Ltd. | ||||||
| ISSN: | 1942-4787 | ||||||
| Official Date: | 5 March 2018 | ||||||
| Dates: |
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| Date of first compliant deposit: | 13 March 2018 | ||||||
| Article Number: | e1241 | ||||||
| DOI: | 10.1002/widm.1241 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Access rights to Published version: | Open Access |
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