| dc.contributor.author | Лінійчук, М. О. | uk |
| dc.contributor.author | Рейда, О. М. | uk |
| dc.contributor.author | Liniichuk, M. O. | en |
| dc.contributor.author | Reyda, O. M. | en |
| dc.date.accessioned | 2026-09-03T10:42:56Z | |
| dc.date.available | 2026-09-03T10:42:56Z | |
| dc.date.issued | 2026 | |
| dc.identifier.citation | Лінійчук М. О., Рейда О. М. Analytical tools for monitoring the popularity of cultural events in web systems // Матеріали Міжнародної науково-практичної інтернет-конференції «Молодь в науці: дослідження, проблеми, перспективи (МН-2026)», м. Вінниця, 22-26 червня 2026 р. Електрон. текст. дані. 2026. URI: https://conferences.vntu.edu.ua/index.php/mn/mn2026/paper/view/29812. | uk |
| dc.identifier.uri | https://ir.lib.vntu.edu.ua/handle/123456789/53522 | |
| dc.description.abstract | The paper examines analytical tools used in web systems to monitor the popularity of cultural events. Key approaches to data collection and processing are analyzed, including web analytics, rating systems, and recommendation algorithms. | en |
| dc.description.abstract | У тезах розглядаються аналітичні інструменти, що застосовуються у веб-системах для моніторингу популярності культурних заходів. Проаналізовано основні підходи до збору та обробки даних, зокрема використання веб-аналітики, систем рейтингування та алгоритмів рекомендацій. Виявлено переваги та обмеження сучасних інструментів і окреслено напрями їх інтеграції у веборієнтовані інформаційні системи. | uk |
| dc.language.iso | en_US | en_US |
| dc.publisher | ВНТУ | uk |
| dc.relation.ispartof | Матеріали Міжнародної науково-практичної інтернет-конференції «Молодь в науці: дослідження, проблеми, перспективи (МН-2026)», м. Вінниця, 22-26 червня 2026 р. | uk |
| dc.relation.uri | https://conferences.vntu.edu.ua/index.php/mn/mn2026/paper/view/29812 | |
| dc.subject | веб-аналітика | uk |
| dc.subject | культурні заходи | uk |
| dc.subject | рейтингування | uk |
| dc.subject | моніторинг популярності | uk |
| dc.subject | інформаційнісистеми | uk |
| dc.subject | web analytics | en |
| dc.subject | cultural events | en |
| dc.subject | rating | en |
| dc.subject | popularity monitoring | en |
| dc.subject | information systems | en |
| dc.title | Analytical tools for monitoring the popularity of cultural events in web systems | en |
| dc.type | Thesis | |
| dc.identifier.udc | 004.78:069 | |
| dc.relation.references | based on attributes such as genre, location, and artist. Hybrid approaches that combine both methods have demonstrated superior accuracy in predicting user interest in cultural activities. Behavioral analytics tools – including heatmaps, scroll-depth tracking, and A/B testing frameworks – complement quantitative data by revealing how users interact with event listings at a micro-level. Tools such as Hotjar or Microsoft Clarity provide visual representations of user attention distribution, which can be used to optimize event presentation and improve discoverability. Real-time dashboards built on technologies such as Apache Kafka and Elasticsearch allow continuous monitoring of event popularity metrics with minimal latency [6]. Integration into Web-Oriented Information Systems Effective integration of analytical tools into web-oriented information systems for cultural event management requires a layered architecture. The data collection layer captures raw interaction signals via APIs, tracking scripts, and database triggers. The processing layer applies statistical models and ML algorithms to transform raw data into meaningful popularity indicators. The presentation layer delivers visual analytics to administrators and, in aggregated form, to end users through interactive dashboards and public rating displays [2; 5]. Key challenges in this integration include ensuring data privacy compliance (GDPR), handling cold-start problems in recommendation systems for newly added events, and maintaining system performance under high traffic loads during peak cultural seasons. Addressing these challenges requires careful selection of tools and architectural patterns appropriate to the scale and goals of the target system. Conclusions The analysis demonstrates that monitoring the popularity of cultural events in web systems requires a combination of web analytics platforms, rating aggregation mechanisms, sentiment analysis, and behavioral analytics tools. Each category addresses a distinct aspect of popularity measurement and together they form a comprehensive analytical framework. Integrating these tools into a web-oriented information system enables real-time, data-driven management of cultural event visibility and audience engagement. Future work should focus on developing unified API-based integration protocols and privacy-preserving analytics approaches tailored specifically to the cultural sector. REFERENCES | en |
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