Search results for "log file"

Modeling a non-stationary bots’ arrival process at an e-commerce Web site

2017

Abstract The paper concerns the issue of modeling and generating a representative Web workload for Web server performance evaluation through simulation experiments. Web traffic analysis has been done from two decades, usually based on Web server log data. However, while the character of the overall Web traffic has been extensively studied and modeled, relatively few studies have been devoted to the analysis of Web traffic generated by Internet robots (Web bots). Moreover, the overwhelming majority of studies concern the traffic on non e-commerce websites. In this paper we address the problem of modeling a realistic arrival process of bots’ requests on an e-commerce Web server. Based on real…

Application of neural network to predict purchases in online store

2016

A key ability of competitive online stores is effective prediction of customers’ purchase intentions as it makes it possible to apply personalized service strategy to convert visitors into buyers and increase sales conversion rates. Data mining and artificial intelligence techniques have proven to be successful in classification and prediction tasks in complex real-time systems, like e-commerce sites. In this paper we proposed a back-propagation neural network model aiming at predicting purchases in active user sessions in a Web store. The neural network training and evaluation was performed using a set of user sessions reconstructed from server log data. The proposed neural network was abl…

Using association rules to assess purchase probability in online stores

2016

The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formu…

Modeling a session-based bots' arrival process at a Web server

2017

How do university students’ web search behavior, website characteristics, and the interaction of both influence students’ critical online reasoning?

2020

The Internet has become one of the main sources of information for university students’ learning. Since anyone can disseminate content online, however, the Internet is full of irrelevant, biased, or even false information. Thus, students’ ability to use online information in a critical-reflective manner is of crucial importance. In our study, we used a framework for the assessment of students’ critical online reasoning (COR) to measure university students’ ability to critically use information from online sources and to reason on contentious issues based on online information. In addition to analyzing students’ COR by evaluating their open-ended short answers, we also investigated the stude…

Characterizing Web sessions of e-customers interested in traditional and innovative products

2016

Web traffic characterization and modelling is currently a hot research issue. Low-level analysis of HTTP traffic on the server allows one to build adequate traffic models to be used in server benchmarking. High-level analysis of Web user behavior allows one to optimize website structure and develop personalized service strategies. In this paper, analysis of customer sessions in an online store is performed using Web server log data. The goal is to explore possible differences between sessions of customers viewing and purchasing innovative products, and customers only interested in traditional products.