## 1 Introduction  
The system should exhaust performance goals including functionality, extendibility, security, flexibility, reliability, usability, connectivity, and privacy. However, many Web-based systems exposed significant performance issues after they were already deployed [1]. Most of these performance problems were due to software and hardware issues deployed without a web system workload analysis. Performance engineering [2] provides the information needed to build a system that meets performance requirements. The idea is to identify performance flaws even before the system is deployed. The huge content of information on the web system makes it a sophisticated area for data mining research. Web systems present challenging aspects and tasks for extracting information from an output data stream. Earlier research related to the performance of the web system [2, 3] gave rise to the idea of analyzing logs with the use of data mining. It is an innovative approach to the analysis of [3] customers and system behavior by determining the variability of the system output parameters obtained from logs. Our approach differs from the solutions used in web content/structure mining [4]. We focus on log analysis in terms of user's behavior such as web usage and utilization mining [5]. This research is designed with the main aim of extracting knowledge from the particular web system log. In this study, a log analysis of the web system was performed on sent-to-the-system requests. The examined platform was based on two types of hardware and the same stock exchange software. Additionally, to perform an analysis, the data set contained timestamp data. After visiting any API endpoint, the user leaves some information. This information can be collected, stored in logs and analyzed. Data mining is a multidisciplinary field involving artificial intelligence, machine learning, databases, statistics, and information retrieval. It is commonly defined as the process of discovering useful patterns or knowledge from different data sources such as texts, images, audio, video, etc. Data mining comprises some classes of tasks performed [4]: association rule--the search for relationships between several variables, clustering--the task of finding and extracting groups and structures in the data, classification--the activity of generalizing known structures to apply to the new data set, and regression--it tries to extract a function that models the data with the least error. Another solution is web mining. It is the application of data mining techniques to discover patterns from the web system behavior. Web mining can be divided into three major categories [4]: web content mining aims to extract useful information or knowledge from web data contents such as text, images, audio, etc., web structure mining tries to discover useful knowledge from the structure of hyperlinks and tags, and web usage mining refers to the discovery of user's usage logs, web server logs, application server logs, etc. Web usage mining also called log mining is a process of recording user access data on the Web and collecting data in the form of logs. Web usage mining is parsed into three distinctive phases preprocessing, pattern discovery, and pattern analysis. We use data mining tools in web usage mining techniques that can bring many new possibilities for the analysis of web system log files. In this article, we follow one case (the stock exchange web system) and describe our study on how to build data mining models to understand user behavior and thoughtful system design. Therefore, in this investigation, our objective was also to check application data mining algorithms, which can help system architects and performance engineers build modern web systems. The article focuses on how to extract useful information from web system outcoming log data with Association Rules (AR) mining. Regression Trees (RT) were used to extend the proposed analysis. Next, we discuss the strengths and weaknesses of AR and RT.