Вісники НТУ "ХПІ"

Постійне посилання на розділhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/2494


З 1961 р. у ХПІ видається збірник наукових праць "Вісник Харківського політехнічного інституту".
Згідно до наказу ректора № 158-1 від 07.05.2001 року "Про упорядкування видання вісника НТУ "ХПІ", збірник був перейменований у Вісник Національного Технічного Університету "ХПІ".
Вісник Національного технічного університету "Харківський політехнічний інститут" включено до переліку спеціалізованих видань ВАК України і виходить по серіях, що відображають наукові напрямки діяльності вчених університету та потенційних здобувачів вчених ступенів та звань.
Зараз налічується 30 діючих тематичних редколегій. Вісник друкує статті як співробітників НТУ "ХПІ", так і статті авторів інших наукових закладів України та зарубіжжя, які представлені у даному розділі.

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2022 - 20232

Налаштування

Автор: search.filters.author.Kopp, Andrii MykhailovychКлючові слова: search.filters.subject.natural language processing

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Зараз показуємо 1 - 2 з 2
  • 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.
  • Ескіз
    Публікація
    The approach and the software tool to calculate semantic quality measures of business process models
    (Національний технічний університет "Харківський політехнічний інститут", 2022) Kopp, Andrii Mykhailovych; Orlovskyi, Dmytro Leonidovych
    Business process models are essential business process management artifacts that help describe visually ongoing business activities to facilitate communication between information technology and business stakeholders. Business process models are used to find inefficient spots within described workflows and resolve detected shortcomings by automation via configurable software solutions or unified workflow engines. However, this is impossible when using syntactically or semantically poor business process models. It is the same as building a house using the blueprint with windows on the floor and typos in text labels. Therefore, it is extremely important to keep created business process models clear and relevant to the actual workflows they describe. Hence, in this paper, we propose the approach and the software tool to calculate semantic quality measures of business process models. The proposed approach uses a special procedure to extract the modeling domain statements using natural language processing techniques. According to the proposed approach, the initial textual descriptions of business process models should be tokenized. Then obtained tokens should be turned to the lower case style and cleansed to remove non-alphabetic tokens and stop words. Finally, the remaining tokens should be stemmed and the existing duplicates should be removed. The same procedure is then repeated for text labels attached to the business process model activities. Then, tokens present in the result of textual description’s processing but missing in the result of labels’ processing are considered incomplete (i.e. incorrect in the modeling domain). Similarly, tokens present in the result of labels’ processing but missing in the result of textual description’s processing are considered invalid (i.e. irrelevant to the modeling domain). Therefore, respective semantic quality measures can be calculated. The software tool is created using the Python programming language because of its powerful natural language processing packages.