Open Access

ARTICLE

An E-Business Event Stream Mechanism for Improving User Tracing Processes

Ayman Mohamed Mostafa^1,2,*, Saleh N. Almuayqil¹, Wael Said^2,3

1 College of Computers and Information Sciences, Jouf University, Sakaka, 72314, Saudi Arabia
2 Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt
3 College of Computer Science and Engineering, Taibah University, Medina, 42353, Saudi Arabia

* Corresponding Author: Ayman Mohamed Mostafa. Email:

(This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 69(1), 767-784. https://doi.org/10.32604/cmc.2021.018236

Received 01 March 2021; Accepted 02 April 2021; Issue published 04 June 2021

Abstract

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets; namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets.

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Keywords

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