Grażyna Suchacka

BUSINESS-ORIENTED ADMISSION CONTROL AND REQUEST SCHEDULING FOR e-COMMERCE WEBSITES

The article concerns the problem of quality of Web service (QoWS) in e-commerce with the focus on request admission control and scheduling from the profit perspective of the owner of an e-business site. Two new functional extensions for a Web server system supporting business-to-consumer (B2C) activity are proposed. The first new feature is an ability to differentiate the key customers of the Web store and to offer higher QoWS to more valued ones. The second new function concerns offering high QoWS to all customers finalizing their purchase transactions. A new approach to B2C service differentiation using RFM (Recency, Frequency, Monetary Value) analysis is presented. A simulation model of the B2C Web server system and some results of the system performance for our approach in comparison with first in–first out (FIFO) scheduling are discussed.

Web Traffic Modeling for E-Commerce Web Server System

The paper concerns a problem of the e-commerce Web server system performance evaluation through simulation experiments, especially a problem of modeling a representative stream of user requests at the input of such system. Motivated by a need of a benchmarking tool for the Business-to-Consumer (B2C) environment we discuss a workload model typical of such Web sites and also a model of a multi-tiered e-commerce Web server system. A simulation tool in which the proposed models have been implemented is briefly talked over and some experimental results on the Web system performance in terms of traditional and business performance measures are presented.

Detection of Internet robots using a Bayesian approach

A large part of Web traffic on e-commerce sites is generated not by human users but by Internet robots: search engine crawlers, shopping bots, hacking bots, etc. In practice, not all robots, especially the malicious ones, disclose their identities to a Web server and thus there is a need to develop methods for their detection and identification. This paper proposes the application of a Bayesian approach to robot detection based on characteristics of user sessions. The method is applied to the Web traffic from a real e-commerce site. Results show that the classification model based on the cluster analysis with the Wards method and the weighted Euclidean metric is very effective in robot detection, even obtaining accuracy of above 90%.

Practical Aspects of Log File Analysis for E-Commerce

The paper concerns Web server log file analysis to discover knowledge useful for online retailers. Data for one month of the online bookstore operation was analyzed with respect to the probability of making a purchase by e-customers. Key states and characteristics of user sessions were distinguished and their relations to the session state connected with purchase confirmation were analyzed. Results allow identification of factors increasing the probability of making a purchase in a given Web store and thus, determination of user sessions which are more valuable in terms of e-business profitability. Such results may be then applied in practice, e.g. in a method for personalized or prioritized service in the Web server system.

Application of selected supervised classification methods to bank marketing campaign

Abstract This article answers 50 key questions that every software maintenance manager should ask to improve the software maintenance process. Issues include the feasibility and applicability of software reuse and the development of maintainable software as well as the improvement of existing software and achieving programmer and software productivity. An overview of software tools that aid in maintaining code is also provided.

Classification Of E-Customer Sessions Based On Support Vector Machine.

A key feature of high-traffic e-commerce sites is the ability to offer a predictive and personalized service to Web users. Visitors to online stores are potential buyers but in reality very few visits finally result in a product purchase. Thus, it would be especially valuable for online retailers to predict buyers against browsers based on some session features (e.g. session duration, the number of downloaded pages, the kind of realized Web interactions) and some HTTP-level information (the number of HTTP requests, the volume of data transfer in session). In this paper, we recast online purchase predictions as a classification problem. Every user session in a web store is represented as a 23-element vector in the session feature space. Based on historical data from an online bookstore an SVM classification model is proposed, dividing user sessions into two classes: browsing sessions and buying sessions. The best SVM classifier proved to be very effective, with a predictive accuracy of over 99% and the probability of predicting a buying session of almost 95%.

Simulation-Based Performance Study of e-Commerce Web Server System – Results for FIFO Scheduling

The chapter concerns the issue of overloaded Web server performance evaluation using a simulation-based approach. We focus on a Business-to-Consumer (B2C) environment and consider server performance both from the perspective of computer system efficiency and e-business profitability. Results of simulation experiments for the Web server system under First-In-First-Out (FIFO) scheduling are discussed. Much attention has been paid to the analysis of the impact of a limited server system capacity on business-related performance metrics.

Discovering and usage of customer knowledge in QoS mechanism for B2C web server systems

The paper deals with the problem of guaranteeing high Quality of Service (QoS) in e-commerce Web servers. We focus on the problem of request admission control and scheduling in a Business-to-Consumer (B2C) Web server from the profit perspective of the owner of an e-business company. We propose extending a Web server system with the ability to identify and favour key customers of a Web store and to ensure the possibility of successful interaction for all customers finalizing their purchase transactions. We propose applying a Recency-Frequency-Monetary analysis (RFM) to discover key customer knowledge and using the resulting RFM scores in a novel QoS mechanism. We discuss the mechanism and some simulation results of its performance.

Web Server Support for e-Customer Loyalty through QoS Differentiation

The paper deals with the problem of offering predictive service in e-commerce Web server systems under overload. Due to unpredictability of Web accesses, such systems often fail to effectively handle peak traffic, which results in long delays and incomplete transactions. As a consequence, online retailers miss an opportunity to attract new customers, retain the loyalty of regular customers, and increase profits. We propose a method for priority-based admission control and scheduling of requests at the Web server system in order to differentiate Quality of Service (QoS) with regard to user-perceived delays, i.e., Web page response times provided by the system (as opposed to HTTP request response times). To detect and cope with the system overload, a new kind of a load indicator is proposed, based on online measurements of page response times. Simulation results demonstrate that our solution is capable of providing key customers with limited delays while improving QoS for ordinary customers under heavy load.

Investigating Long-Range Dependence in E-Commerce Web Traffic

This paper addresses the problem of investigating long-range dependence (LRD) and self-similarity in Web traffic. Popular techniques for estimating the intensity of LRD via the Hurst parameter are presented. Using a set of traces of a popular e-commerce site, the presence and the nature of LRD in Web traffic is examined. Our results confirm the self-similar nature of traffic at a Web server input, however the resulting estimates of the Hurst parameter vary depending on the trace and the technique used.