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A BUSINESS LOGIC MODEL DEVELOPMENT FOR SOFTWARE OPERATIONS

Аннотация

This article presents a business logic model development for software operation based on an event-driven approach. The proposed model ensures flexible interaction between users, events, and message generation processes, optimizing software performance. The study focuses on the mathematical representation of business logic and its practical application in automating email notifications. The results demonstrate the efficiency of the proposed model in enhancing adaptability and scalability


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OPERATIONS

Vladyslav Yevsieiev1, Svitlana Maksymova1, Ahmad Alkhalaileh2 1Department of Computer-Integrated Technologies, Automation and Robotics, Kharkiv

National University of Radio Electronics, Ukraine

2Senior Developer Electronic Health Solution, Amman, Jordan

ABSTRACT

This article presents a business logic model development for software operation based on an event-driven approach. The proposed model ensures flexible interaction between users, events, and message generation processes, optimizing software performance. The study focuses on the mathematical representation of business logic and its practical application in automating email notifications. The results demonstrate the efficiency of the proposed model in enhancing adaptability and scalability. Keywords: Business Logic, Software Development, Event-Driven Model, Automation, Adaptability, Scalability, Email Notifications, Mathematical Modeling.

INTRODUCTION

Developing a software business logic model is an important stage in the modern process of creating effective and scalable software solutions. In the context of rapid digital transformation and increasing requirements for software quality [1]-[10], business logic is a key component that ensures that the functionality of the software product meets the needs of users and the business processes organization.

Such models can be used for various areas of research and production organization [11]-[23]. In this case, special and classical approaches are used for their implementation [24]-[44].

Traditional approaches to software development often focus on technical aspects, while business logic design allows you to create systems that easily adapt to market changes, provide effective integration with other services, and simplify further

Using business logic models in the development process allows you to clearly define roles, relationships between objects, and rules for their interaction, which reduces the risk of errors and increases the reliability of the software. In modern methodologies, such as Domain-Driven Design (DDD) and Model-Driven Architecture (MDA), business logic takes a central place, allowing developers to focus on the essence of business processes, and not only on their technical implementation.

In addition, automation of business logic modeling using modern tools and frameworks helps to optimize costs and reduce development time. Thus, the study of business logic models in the context of software development is not only relevant, but also necessary for creating adaptive, flexible and competitive IT solutions.

RELATED WORKS

Analysis of software operation is an integral part of the work and implementation of this software. Accordingly, scientists consider this task from different sides. Including the creation of messages for users in accordance with the business logic of this software, we will consider several works on this topic

In the traditional application model, services are tightly coupled with the processes they support [45]. Authors in [45] clarify the relationship between currently developing standards such as UDDI, WSDL, and WSCL, and propose a conversation controller mechanism that leverages such standards to direct services in their conversations.

Ćatović, A., & et al. [46] describe RabbitMQ that acts as an intermediary between the various services. They demonstrated that the style of microservice architecture is an approach to the development of an application as a set of small services, each in charge of its own process and communication with other services.

Scientists in [47] consider the problem of log message template identification. It is aims to convert raw logs containing free-formed log messages into structured logs to be processed by automated log-based analysis, such as anomaly detection and model inference.

The paper [48] introduces MontiThings, a C&C language offering automatic error handling capabilities and a clear separation between business logic and implementation details. The error-handling methods presented in this paper can make C&C-based IoT applications more reliable without cluttering the business logic with error-handling code that is time-consuming to develop and makes the models hard to understand, especially for non-experts.

Li, X., and co-authors [49] analyze a blockchain-based flight operation data sharing scheme, named BFOD is designed to achieve the privacy protection and secure sharing of flight operation data. In BFOD, physical entities of airlines, airports and air traffic control are divided into data owners, data requesters and authorization institutes according to the business logic.

Thus, we see that the problem of business logic analysis and management is multifactorial. Later in this article, we will present our business logic model development for software operations.

MATHEMATICAL REPRESENTATION OF SOFTWARE DEVELOPMENT

BUSINESS LOGIC USING AN EVENT-DRIVEN MODEL

Business logic of software is a set of rules, algorithms and processes that determine how the software processes data, interacts with users, performs operations and ensures the achievement of set goals. It reflects the specific needs of the business or organization that the software solves, and is a central element of any program architecture. Business logic determines the order of functions, decision-making conditions and methods of processing information in accordance with the needs of end users. Its development allows you to make the program flexible, scalable and adaptable to changes in processes or requirements. Business logic is a necessary component of software development, as it establishes a connection between the technical capabilities of the system and its functional goals. It allows developers to create solutions that meet specific tasks, optimizing internal processes and ensuring effective interaction between users and the system. In the context of automation of e-mail messages, business logic determines which events require sending messages, how these messages are generated, personalized and delivered to users. Its development ensures that the system will respond to events in a timely, relevant and in accordance with established business requirements. This reduces the risk of errors, increases the efficiency of the system and helps meet the needs of end users.

An event-driven business logic model was chosen for the software for automating the process of sending e-mail messages. This model is optimal because it ensures that the system responds to specific events stored in the database or received via API. The eventdriven model allows you to build a clear and structured process for processing events that trigger appropriate actions, including message generation, content personalization and their distribution.

The event-driven model provides flexibility and scalability of the system, allowing you to easily add new types of events or functionality without significant changes to the basic architecture. In addition, such a model integrates well with modern analytics services and APIs for data collection, allowing you to respond to the results of event analysis in real time. This is important for achieving the main goals of the developed software, including personalization, responsiveness and communication efficiency.

An event-driven business logic model can be described as a mathematical system based on sets, functions and rules. To develop a business logic model based on an eventdriven model, we introduce the following parameters, which are presented in Table 1.

In the context e-mail messages sending automation, the model consists of the following main components:

– sets and parameters of the software being developed:}u,...,u,u{U n21=

(1)},...,,{ 21 meeeE =

(2)},...,,{ 21 kmmmM =

(3)},...,,{ 21 ntttT =

(4)U

– users set;1u

– system user;E

– events set;

77je

– event that can trigger a mailing;M

– messages set;km

– system-generated message;T

– time set;pt

– a time parameter that defines the moment when the event occurred or the message was sent. – functions and dependencies event-driven business logic model for software

Table 1: Parameters

Parameter Descriptioniu

unique user IDje

event ID.km

unique message associated with the eventpt

time of event activation or message sendinguf

defines the logic of relationships between users and eventssf

monitors message delivery status

Event handling function:MEfE →:

(5)EfE :

– defines messagekm , that must be generated for the eventje .

User subscriptions function:E

U Uf 2: →

(6)UfU :

– defines the set of events to which the useriu is subscribed;E

– set of all subsetsE .

Messages generation function:ContentUMfM →:

(7)),( ikM umf

– forms the content of the messagekm for the useriu taking into account personalization.

Time function:}1,0{: →TfT

(8)1)( =pT tf

, if messagekm is successfully delivered, and 0 in other case.

The system business logic is described as a functions sequence:

– the incoming eventje is checked by the time functiontf to determine its relevance;

– for each useriu who has a subscription to the eventje , defined throughUf , the event processing functionEf is called, which creates a message ;

– the generated message is passed to the messages generation function , which forms a personalized text;

– the messagekm is sent to the useriu and checked by the delivery status functionSf

.

The developed mathematical model provides a formal representation of eventdriven business logic, on the basis of which software development is built. The model visualizes the key components of business logic, such as sets of users, events, messages, time and functions that describe their interaction. The model helps to understand how events are processed, messages are generated, their relevance is checked and delivery is ensured. A graphical representation of the mathematical model of event-driven business logic for software for automating the process of sending e-mail messages based on the results of event analysis is presented in Figure 1.

Figure 1: Graphical representation of the mathematical model of event-driven business logic for software to automate the process of sending e-mail messages based

on the event analysis results

Based on the developed database structure, the following solution is proposed, that the software is built on the principle of an event-oriented model, where the key components are events (Events), users (Users), user subscriptions (UserSettings) and work log (WorkProtocol). This provides flexibility in creating new analytical events, managing users and their subscriptions, as well as maintaining a log of sendings. The logic of the work involves integration with APIs for data collection, their analysis in the form of events and automatic sending of results to e-mail or messengers. The general view of the algorithm is presented in Figure 2.

Let us describe the purpose of each block of the general algorithm presented in Figure 2.

System initialization:

– loading basic event data from the Events table;

– connecting to the API for collecting information.

User management:

– adding/editing users via the management interface;

– setting up event subscriptions from the UserSettings table.

Event launch:

– events can be triggered manually by the user or automatically, if regular triggering is provided;

– collecting data from external sources (for example, external APIs).

Data analysis – performing calculations and generating results in a format ready for sending (text report or table). Figure 2: Software operation general algorithm for the sending e-mail messages process

automation

Starr

System initialization

User management

Event launch

Data analysis

Message generation A

A

Sending results

Journaling

Stop?

Finish

Yes

No

Message generation – personalizing content for each user based on their subscriptions and specific settings.

Sending results:

– integration with email or messenger services;

– sending messages to users who are subscribed to the corresponding events.

Logging:

– recording data about sending messages in the WorkProtocol table;

– saving information about the date, user, event, and sending status.

The algorithm begins with the launch of an event that initiates the data collection process from the API. The system analyzes the received data, comparing it with the specified event parameters, and generates an analytical report. Based on user subscriptions, those to whom the results need to be sent are determined. Next, content personalization occurs, taking into account the user name, his subscriptions, and the message format. Generated messages are added to the queue for sending via integration with e-mail services or messenger APIs. All operations are logged in the log for further analysis.

The developed algorithm for automating the sending of e-mail messages and messages to messengers has a number of advantages that ensure the efficiency, flexibility, and reliability of the system. It allows you to process events in both manual and automatic modes, which increases the usability for various scenarios. Thanks to integration with the API for data collection, the algorithm can dynamically analyze information and quickly respond to changes. Message personalization ensures that content is precisely tailored to user needs, which contributes to better interaction with customers. The presence of logging allows you to track the history of sending, analyze results, and ensure transparency of work. The use of queues for sending messages minimizes system overload and ensures stable operation even with a large number of requests. This algorithm structure supports scalability and easily adapts to new requirements or additional functions.

CONCLUSION

During the research, an event-oriented model of software business logic was developed, which allows for effective organization of interaction between users, events and data processing mechanisms. The proposed approach provides high flexibility and scalability of the system, which is an important factor in modern software. The mathematical representation of the model allowed for the formalization of key processes; including event generation, message personalization and delivery management. The algorithmic approach to the implementation of business logic ensures stability and reliability of the system even under heavy load. The proposed model effectively integrates with external services and APIs, which allows for improved interaction between components of the software complex. The developed system for automating the process of sending e-mail messages demonstrates high efficiency in the context of timely response to events and content personalization. The use of an event-oriented approach allows for minimizing data processing time and increasing the speed of the system's response to changes in business processes. The implementation of such business logic contributes to an increase in the level of automation, which reduces the need for manual intervention and optimizes software support costs. Thus, the results of the study confirm the feasibility of using an event-driven model for building adaptive and productive software solutions.

REFERENCES

1. Demska, N., & et al. (2025). Analysis of Methods, Models and Algorithms for a

Collaborative Robots Group Decentralized Control. ACUMEN: International journal

of multidisciplinary research, 2(2), 235-249.

2. Yevsieiev, V., & et al. (2024). Calculation of the Distance to Objects in Collaborative

Robots Workspace Using Computer Vision. Journal of universal science research,

2(11), 240-255.

3. Basiuk, V., & et al. (2024). Command System for Movement Control Development.

Multidisciplinary Journal of Science and Technology, 4(6), 248-255.

4. Chala, O., & et al. (2024). Analysis of Systems for Coordination of Enterprise

Subsystems Control. Journal of universal science research, 2(10), 127-137.

5. Yevsieiev, V., & et al. (2024). A Program for Analyzing the Structure of a Web site

Development Using the Parsing Method Based on the Python. Journal of Universal

Science Research, 2(4), 172-183.

6. Gurin, D., & et al. (2024). Web site reliability analysis using the python parsing

method. Journal of Universal Science Research, 2(5), 113-126.

7. Yevsieiev, V., & et al. (2024). Data Fusion Research for Collaborative Robots-

Manipulators within Industry 5.0. ACUMEN: International journal of

multidisciplinary research, 1(4), 125-137.

8. Moiseev, M., & et al. (2024). Program Algorithm for Monitoring System

Development. Journal of universal science research, 2(7), 33-43.

9. Chala, O., & et al. (2024). Switching Module Basic Concept. Multidisciplinary

Journal of Science and Technology, 4(7), 87-94.

10.Yevsieiev, V., & et al. (2024). Human Operator Identification in a Collaborative

Robot Workspace within the Industry 5.0 Concept. Multidisciplinary Journal of

Science and Technology, 4(9), 95-105.

11.Attar, H., Abu-Jassar, A. T., Amer, A., Lyashenko, V., Yevsieiev, V., & Khosravi,

M. R. (2022). Control system development and implementation of a CNC laser

engraver for environmental use with remote imaging. Computational intelligence and

neuroscience, 2022(1), 9140156.

12.Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2021). Neural

networks as a tool for pattern recognition of fasteners. International Journal of

Engineering Trends and Technology, 69(10), 151-160.

13.Abu-Jassar, A. T., Al-Sharo, Y. M., Lyashenko, V., & Sotnik, S. (2021). Some

Features of Classifiers Implementation for Object Recognition in Specialized

Computer systems. TEM Journal: Technology, Education, Management, Informatics,

10(4), 1645-1654.

14.Abu-Jassar, A. T., Attar, H., Yevsieiev, V., Amer, A., Demska, N., Luhach, A. K., &

Lyashenko, V. (2022). Electronic user authentication key for access to HMI/SCADA

via unsecured internet networks. Computational intelligence and neuroscience,

2022(1), 5866922.

15.Nevliudov, I., Yevsieiev, V., Baker, J. H., Ahmad, M. A., & Lyashenko, V. (2020).

Development of a cyber design modeling declarative Language for cyber physical

production systems. J. Math. Comput. Sci., 11(1), 520-542.

16.Lyashenko, V., Abu-Jassar, A. T., Yevsieiev, V., & Maksymova, S. (2023).

Automated Monitoring and Visualization System in Production. International

Research Journal of Multidisciplinary Technovation, 5(6), 9-18.

17.Nevliudov, I., & et al.. (2020). Method of Algorithms for Cyber-Physical Production

Systems Functioning Synthesis. International Journal of Emerging Trends in

Engineering Research, 8(10), 7465-7473.

18.Mustafa, S. K., Yevsieiev, V., Nevliudov, I., & Lyashenko, V. (2022). HMI

Development Automation with GUI Elements for Object-Oriented Programming

Languages Implementation. SSRG International Journal of Engineering Trends and

Technology, 70(1), 139-145.

19.Nevliudov, I., Yevsieiev, V., Lyashenko, V., & Ahmad, M. A. (2021). GUI Elements

and Windows Form Formalization Parameters and Events Method to Automate the

Process of Additive Cyber-Design CPPS Development. Advances in Dynamical

Systems and Applications, 16(2), 441-455.

20.Kobylin, O., & Lyashenko, V. (2014). Comparison of standard image edge detection

techniques and of method based on wavelet transform. International Journal, 2(8),

572-580.

21.Abu-Jassar, A. T., Attar, H., Lyashenko, V., Amer, A., Sotnik, S., & Solyman, A.

(2023). Access control to robotic systems based on biometric: the generalized model

and its practical implementation. International Journal of Intelligent Engineering and

Systems, 16(5), 313-328.

22.Babker, A. M., Abd Elgadir, A. A., Tvoroshenko, I., & Lyashenko, V. (2019).

Information technologies of the processing of the spaces of the states of a complex

biophysical object in the intellectual medical system health. International Journal of

Advanced Trends in Computer Science and Engineering, 8(6), 3221-3227.

23.Al-Sherrawi, M. H., Lyashenko, V., Edaan, E. M., & Sotnik, S. (2018). Corrosion as

a source of destruction in construction. International Journal of Civil Engineering and

Technology, 9(5), 306-314.

24.Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2023). Generalized

procedure for determining the collision-free trajectory for a robotic arm. Tikrit

Journal of Engineering Sciences, 30(2), 142-151.

25.Tvoroshenko, I., Lyashenko, V., Ayaz, A. M., Mustafa, S. K., & Alharbi, A. R.

(2020). Modification of models intensive development ontologies by fuzzy logic.

International Journal of Emerging Trends in Engineering Research, 8(3), 939-944.

26.Khan, A., Joshi, S., Ahmad, M. A., & Lyashenko, V. (2015). Some effect of Chemical

treatment by Ferric Nitrate salts on the structure and morphology of Coir Fibre

Composites. Advances in Materials Physics and Chemistry, 5(1), 39-45.

27.Sotnik, S. Overview: PHP and MySQL Features for Creating Modern Web Projects/

S Sotnik, V. Manakov, V. Lyashenko //International Journal of Academic

Information Systems Research (IJAISR). – 2023. – Vol. 7, Issue 1. – P. 11-17.

28.Matarneh, R., Tvoroshenko, I., & Lyashenko, V. (2019). Improving Fuzzy Network

Models For the Analysis of Dynamic Interacting Processes in the State Space.

International Journal of Recent Technology and Engineering, 8(4), 1687-1693.

29.Lyashenko, V. V., Matarneh, R., Baranova, V., & Deineko, Z. V. (2016). Hurst

Exponent as a Part of Wavelet Decomposition Coefficients to Measure Long-term

Memory Time Series Based on Multiresolution Analysis. American Journal of

Systems and Software, 4(2), 51-56.

30.Lyashenko, V. V., Deineko, Z. V., & Ahmad, M. A. Properties of wavelet

coefficients of self-similar time series. In other words, 9, 16.

31.Kuzemin, A., Lуashenko, V., Bulavina, E., & Torojev, A. (2005). Analysis of

movement of financial flows of economical agents as the basis for designing the

system of economical security (general conception). In Third international

conference «Information research, applications, and education (pp. 27-30).

32.Deineko, Zh., & et al.. (2021). Features of Database Types. International Journal of

Engineering and Information Systems (IJEAIS), 5(10), 73-80.

33.Lyashenko, V., Laariedh, F., Ayaz, A. M., & Sotnik, S. (2021). Recognition of Voice

Commands Based on Neural Network. TEM Journal: Technology, Education,

Management, Informatics, 10(2), 583-591.

34.Ahmad, M. A., Baker, J. H., Tvoroshenko, I., & Lyashenko, V. (2019).

Computational complexity of the accessory function setting mechanism in fuzzy

intellectual systems. International Journal of Advanced Trends in Computer Science

and Engineering, 8(5), 2370-2377.

35.Tahseen A. J. A., & et al.. (2023). Binarization Methods in Multimedia Systems when

Recognizing License Plates of Cars. International Journal of Academic Engineering

Research (IJAER), 7(2), 1-9.

36.Abu-Jassar, A. T., Attar, H., Amer, A., Lyashenko, V., Yevsieiev, V., & Solyman,

A. (2025). Development and Investigation of Vision System for a Small-Sized

Mobile Humanoid Robot in a Smart Environment. International Journal of Crowd

Science, 9(1), 29-43.

37.Abu-Jassar AT, Attar H, Amer A, et al. Remote Monitoring System of Patient Status

in Social IoT Environments Using Amazon Web Services (AWS) Technologies and

Smart Health Care. International Journal of Crowd Science, 2024.

38.Attar, H., Abu-Jassar, A. T., Lyashenko, V., Al-qerem, A., Sotnik, S., Alharbi, N., &

Solyman, A. A. (2023). Proposed synchronous electric motor simulation with built-

in permanent magnets for robotic systems. SN Applied Sciences, 5(6), 160.

39.Abu-Jassar A. Building a Route for a Mobile Robot Based on the BRRT and A*(H-

BRRT) Algorithms for the Effective Development of Technological Innovations /

Amer Abu-Jassar, Hassan Al-Sukhni, Yasser Al-Sharo, S. Maksymova, V.

Yevsieiev, V. Lyashenko // International Journal of Engineering Trends and

Technology. – 2024. – V. 72(11). – P. 294-306.

40.Ababneh, J., Abu-Jassar, A., Abuowaida, S., Liubchenko, V., & Lyashenko, V.

(2024, December). Evaluation of Three Different Operators for Object Highlighting

in Medical RGB Images: Canny, Roberts, and LoG in Independent Color Spaces. In

2024 25th International Arab Conference on Information Technology (ACIT) (pp. 1-

7). IEEE.

41.Kuzomin, O., Lyashenko, V., Tkachenko, M., Ahmad, M. A., & Kots, H. (2016).

Preventing of technogenic risks in the functioning of an industrial enterprise.

International Journal of Civil Engineering and Technology, 7(3), 262-270.

42.Serhiienko, O., Novikova, T., & Lyashenko, V. (2023). Comparative Analysis of the

Dynamics of Futures for the Dow Jones, S&P 500 and Nasdaq. International Journal

of Academic Accounting, Finance & Management Research (IJAAFMR), 7(9), 22-

28.

43.Matarneh, R., Sotnik, S., Belova, N., & Lyashenko, V. (2018). Automated modeling

of shaft leading elements in the rear axle gear. International Journal of Engineering

and Technology (UAE), 7(3), 1468-1473.

44.Omarov, M., Tykha, T., & Lyashenko, V. (2019). Use of Wavelet Techniques in the

Study of Internet Marketing Metrics. Eskişehir Technical University Journal of

Science and Technology A-Applied Sciences and Engineering, 20, 157-163.

45.Kuno, H., & et al. (2001). Conversations+ interfaces= business logic. In

Technologies for E-Services: Second International Workshop, TES 2001 Rome,

Italy, September 14–15, 2001 Proceedings 2 (pp. 30-43). Springer Berlin Heidelberg.

46.Ćatović, A., & et al. (2022). Microservice development using RabbitMQ message

broker. Science, engineering and technology, 2(1), 30-37.

47.Khan, Z. A., & et al. (2022). Guidelines for assessing the accuracy of log message

template identification techniques. In Proceedings of the 44th International

Conference on Software Engineering, 1095-1106.

48.Kirchhof, J. C., & et al. (2022). Montithings: Model-driven development and

deployment of reliable iot applications. Journal of Systems and Software, 183,

111087.

49.Li, X., & et al. (2023). BFOD: Blockchain-based privacy protection and security

sharing scheme of flight operation data. IEEE Internet of Things Journal, 11(2), 3392-

3401.

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