Вісник Національного технічного університету «ХПІ». Серія: Системний аналіз, управління та інформаційні технології

Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/67239

Офіційний сайт http://samit.khpi.edu.ua/

Рецензоване наукове видання відкритого доступу, яке публікує нові наукові результати в області системного аналізу та управління складними системами, отримані на основі сучасних прикладних математичних методів і прогресивних інформаційних технологій. Публікуються роботи, пов'язані зі штучним інтелектом, аналізом великих даних, сучасними методами високопродуктивних обчислень у розподілених системах підтримки прийняття рішень.

Рік заснування: 1961. Періодичність: 2 рази на рік. ISSN: 2079-0023 (Print), ISSN: 2410-2857 (Online)

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«Вісник Національного технічного університету "ХПІ". Серія: Системний аналіз, управління та інформаційні технології» внесено до категорії Б «Переліку наукових фахових видань України, в яких можуть публікуватися результати дисертаційних робіт на здобуття наукових ступенів доктора наук, кандидата наук та ступеня доктора філософії»

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Автор: search.filters.author.Orlovskyi, Dmytro Leonidovych

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  • Ескіз
    Публікація
    Blockchain platform selection and software development for decentralized exchange of business process models
    (Національний технічний університет "Харківський політехнічний інститут", 2023) Kopp, Andrii Mykhailovych; Orlovskyi, Dmytro Leonidovych; Olkhovyi, Oleksii Mykhailovych
    Modern organizations employing the Business Process Management (BPM) approach typically handle collections of hundreds or even thousands of business process models. Business process modeling stands as the central technology within the entire BPM methodology. In line with the BPM lifecycle, these models visually represent current organizational activities that necessitate improvement using various diagramming notations. These graphical business process models can subsequently be employed to analyze ongoing activities in the enterprise, identifying potential drawbacks or "weak spots" that hinder the company’s performance. Through business process models, organizations can modify the "virtual twins" of their organizational workflows, conduct simulations, and make informed decisions for business process improvement. Thus, business process models constitute the most valuable assets of the organization, conveying knowledge about ongoing activities and potentially encapsulating the best organizational or industry practices. The implementation of a centralized database for business process models can significantly benefit the entire organization, enhancing the efficiency of knowledge sharing and accumulation. However, centralized business process model repositories prove less efficient for inter-organizational knowledge exchange. Additionally, most business process models require significant person-hours for development and cannot be shared freely with competitors. The exchange of business process models should adhere to established mechanisms for managing valuable digital assets. Presently, Distributed Ledger Technologies (DLT), especially Blockchain, have gained enormous popularity. Therefore, we can employ the principles of Blockchain technology and the cryptocurrency industry to create software for the Decentralized Exchange (DEX) of business process models. This study explores the selection of a DLT platform and the development of software for the decentralized exchange of business process models, utilizing asset tokenization and smart contract technologies.
  • Ескіз
    Публікація
    An algorithm for NLP-based similarity measurement of activity labels in a database of business process models
    (Національний технічний університет "Харківський політехнічний інститут", 2023) Kopp, Andrii Mykhailovych; Orlovskyi, Dmytro Leonidovych
    Business process modeling is an important part of organizational management since it enables companies to obtain insights into their operational workflows and find opportunities for development. However, evaluating and quantifying the similarity of multiple business process models can be difficult because these models frequently differ greatly in terms of structure and nomenclature. This study offers an approach that uses natural language processing techniques to evaluate the similarity of business process models in order to address this issue. The algorithm uses the activity labels given in the business process models as input to produce textual descriptions of the associated business processes. The algorithm includes various preprocessing stages to guarantee that the textual descriptions are correct and consistent. First, single words are retrieved and transformed to lower case from the resulting textual descriptions. After that, all non-alphabetic and stop words are removed from the retrieved words. The remaining words are then stemmed, which includes reducing them to their base form. The algorithm evaluates the similarity of distinct business process models using similarity measures, including Jaccard, Sorensen – Dice, overlap, and simple matching coefficients, after the textual descriptions have been prepared and preprocessed. These metrics provide a more detailed understanding of the similarities and differences across various business process models, which can then be used to influence decision-making and business process improvement initiatives. The software implementation of the proposed algorithm demonstrates its usage for similarity measurement in a database of business process models. Experiments show that the developed algorithm is 31% faster than a search based on the SQL LIKE clause and allows finding 18% more similar models in the business process model database.