A methodology for workload characterization of E-commerce sites
Proceedings of the 1st ACM conference on Electronic commerce
In search of invariants for e-business workloads
Proceedings of the 2nd ACM conference on Electronic commerce
Business-oriented resource management policies for e-commerce servers
Performance Evaluation - Special issue on internet performance modelling
Characterizing the scalability of a large web-based shopping system
ACM Transactions on Internet Technology (TOIT)
Separating the swarm: categorization methods for user sessions on the web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring similarity of interests for clustering web-users
ADC '01 Proceedings of the 12th Australasian database conference
Categorizing Visitors Dynamically by Fast and Robust Clustering of Access Logs
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Knowledge discovery from users Web-page navigation
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Workload characterization for an E-commerce web site
CASCON '03 Proceedings of the 2003 conference of the Centre for Advanced Studies on Collaborative research
Exploiting nonstationarity for performance prediction
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Ranking of website structures using fuzzy TOPSIS method with type-2 fuzzy numbers
FS'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Fuzzy Systems - Volume 8
Integrating recommendation models for improved web page prediction accuracy
ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
WSEAS Transactions on Computers
Graph theory application and web page ranking for website link structure improvement
Behaviour & Information Technology
Cost-based admission control for Internet Commerce QoS enhancement
Electronic Commerce Research and Applications
An integrated model for next page access prediction
International Journal of Knowledge and Web Intelligence
Artificial immune system-based customer data clustering in an e-shopping application
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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In conventional commerce, customer groups with similar interests or behaviours can be observed. Similarly, customers in E-commerce naturally form groups. These groups allow the organization to provide quality of service (QoS) and perform capacity planning. From a system point of view, overall server performance can be improved and resources managed considering customer session behaviour.Previous studies have grouped customers using clustering techniques. Different data metrics have been selected as criteria for grouping, in order to analyze different problems. The limitation for these approaches is that problems areanalyzed separately. In order to manage an E-commerce server well, we must analyze many related problems comprehensively rather than separately. For example, we would like to know what is the impact on resource usage when optimizing revenue. Thus, we must understand the differences and similarities between session groups chosen by different metrics.This paper characterizes customer groups for an E-rental business and compares customer groups created according to different criteria including services requested, navigation pattern and resource usage. A significant finding of this study shows that using each of the three criteria independently yields roughly similar results, since customers looking for similar services tend to have similar navigation pattern as well as similar server resource usage. Thus, it issufficient to group customers in only one of these ways. Grouping customers by services requested is suggested since this method yields relatively better results and is simple to implement.