## 4 Results of the Research Work  
Systems used in Industry 4.0 usually have the character of embedded systems, with limited interference in their structure, including in the area of memory expansion and processor exchange. However, IT systems are always based on the use of the same resources: processor (CPU), memory (RAM), input/output devices (I/O) e.g., HDD (Hard Drive), Internet network. Therefore, the first point that should always be monitored regardless of the project's subject matter is the resources. Thus, optimization of the code, e.g., in terms of required memory, and processor load, is a significant issue. Of course, server systems, especially cloud computing, offer great possibilities of scalability of necessary resources, but such solutions in the area of industry can be used only to a limited extent. Therefore, among other things, no tests have been conducted in a high-performance server environment where powerful processors are available, often with dedicated multi-level memory chips designed to increase processing efficiency. On the other hand, solutions dedicated to industrial and IoT applications usually use widely available memory chips and universal processors. Considering dedicated software, it can be said that these systems very often have a non-deterministic or temporary deterministic character. Thus, there are solutions in which the system's hardware part accelerates the selected range of activities performed by the system (for example, in terms of switching tasks and preserving their context). However, in such cases, it would be necessary to carry out tests focused only on specific hardware and software platforms without the possibility of a broader generalization of the obtained results. Therefore, the choice of classical computer hardware seemed to be the closest to the adopted assumptions. Taking into account the above assumptions, and after the settlements with InfoSoftware Polska Ltd. regarding the use of a hardware platform that meets the requirements of a dedicated module within the iS Rapid system, an environment with the following configuration was selected for testing: Intel(r) Core(tm) i7-7700 CPU @3.60 GHZ, 16 GB RAM, and 64 bit operating system, Windows Server 2016. Taking into account the modular architecture of the system, its individual modules can be processed both in a centralized and distributed environment. This ensures high scalability and the ability to adapt hardware platforms to current needs. Figure 2: The value of entropy for a given set. To verify the capabilities of selected programming languages based on the decision tree algorithm implementation's effectiveness, data received from InfoSoftware Poland Ltd. were used and multiplied to obtain 10 thousand, 100 thousand, and 500 thousand records. Simultaneously, the proportional distribution of individual values was maintained according to the iS Rapid system's actual data. The computation time required to predict the target variable is taken into account; in the case of a database containing 10 thousand records, the best results were obtained for Java. A big surprise was receiving much longer computation time for the Python language. The Golang language was, in this case, in place 2. It should be noted that entirely different results were obtained for more massive datasets. Along with the increase in the number of data, the Python language results improved significantly concerning competing languages. In a situation where the database contained half a million records, Python did best, then Golang, and Java did worst. In the case of 100 records, the differences between the individual languages were the smallest. To verify the results obtained, we made several series of computations. Due to the fact that the results of these series are similar or even identical, their average values are given. The obtained results, presented in Table 3 and Figures 3-5, confirm a certain level of results obtained for particular programming languages and data set sizes.