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06 | 12 | 2022
10.14489/vkit.2022.11.pp.052-065

DOI: 10.14489/vkit.2022.11.pp.052-065

Филимонов И. А.
АВТОМАТИЗАЦИЯ АДМИНИСТРИРОВАНИЯ МЕТАДАННЫХ ЭЛЕКТРОННЫХ БИБЛИОТЕК, ПОДДЕРЖИВАЮЩИХ ОБЪЕКТНЫЙ ПОИСК
(с. 52-65)

Аннотация. Представлено совершенствование работы с сетью метаданных электронной научно-технической библиотеки путем автоматизации добавления элементов решения проблем с помощью программируемых сценариев. Для режима эксплуатации библиотеки предлагается более пригодный, по сравнению с ручным, способ пополнения сети кластерами узлов, обеспечивающий бо́льшую степень автоматизации при работе с метаданными электронной библиотеки. Представлена математическая модель оценки интерфейса. Предложен пример схемы добавления кластера узлов и сравнительная оценка методов и инструментов актуализации сети проблем.

Ключевые слова:  электронная библиотека; архив документов; контент библиотеки; метаданные библиотеки; объектный поиск; граф знаний; сеть проблем; инновационный цикл; автоматизация; программирование.

 

Filimonov I. A.
AUTOMATION OF A NETWORK OF PROBLEMS USING PROGRAMMING TOOLS
(pp. 52-65)

Abstract. One of the directions of development of metadata of electronic libraries is their selective visualization with the provision of object search in the visual network representation of metadata. One of such systems is also developed by the author of EaAIS “PoiskUM”. In the “PoiskUM” system, an attempt has been made to programmatically implement an electronic library designed for personal use and providing object search functions. In contrast to the classical dictionary search, the object search system provides the reader with the opportunity to fully or partially search through the elements of the library collection and “identify” the desired objects among them on the basis of search features located in cognitive memory. In this system, the user can see a complex network consisting of a network of scientific and technical problems on the computer screen, a network of innovative cycles of technical products related to the problems shown, and a network of library documents. This network is visualized by a representative of a special class of applied software systems – a graph editor. The graph editor builds network elements in its memory in the form of objects and shows them in its windows in the form of a table displaying the attributes of objects. Maintaining and developing the functions of replenishing the network of problems require significant manual labor from the owner of the electronic library. This article discusses proposals for automating the replenishment of structural elements of the network of problems in relation to the metadata of an electronic library that supports object search.

Keywords: Digital library; Document archives; Library content; Library metadata; Entity search; Knowledge graph; Problem network; Innovation cycle; Automation; Programming.

Рус

И. А. Филимонов (Московский авиационный институт (национальный исследовательский университет), Москва, Россия) E-mail: ilafilimonov@mai.education 

Eng

I. A. Filimonov (Moscow Aviation Institute (National Research University), Moscow, Russia) E-mail: ilafilimonov@mai.education 

Рус

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Eng

1. Siew C. S., Wulff Dirk U., Beckage Nicole M. et al. (2019). Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics. Complexity, Vol. 2019, pp. 1 – 24. DOI:10.1155/2019/2108423
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5. Gagarin A. P., Filimonov I. A. (2021). An enriched network of problems as the core of digital library metadata. Sovremennye informatsionnye tekhnologii i IT-obrazovanie, Vol. 17, (4), pp. 860 – 870. [in Russian language] DOI: 10.25559/SITITO.17.202104.860-870
6. Otasek D., Morris J., Boucas J., Pico A. et al. (2019). Cytoscape Automation: Empowering Workflow-Based Network Analysis. Computer Science, 185, pp. 1 – 15. Available at: https://www.ncbi.nlm. nih.gov/pubmed/31477170 (Accessed: 29.10.2022).
7. Balog K. (2018). Entity-Oriented Search. The Information Retrieval Series, 39, pp. 11 – 17. Available at: https://link.springer.com/content/pdf/10.1007/978-3-319-93935-3.pdf (Accessed: 29.10.2022).
8. Zakariya A., Jakimi A., Hajar M. (2018). An Algorithm of Conversion Between Relational Data and Graph Schema. Information Systems and Technol-ogies to Support Learning, 111, pp. 594 – 602. Available at: https://link.springer.com/chapter/10.1007/978-3-030-03577-8_65 (Accessed: 29.10.2022).
9. Ruggero A. (2019). Entity search: How to Build Virtual Documents Leveraging on Graph Embeddings. Computer Science, pp. 56 – 65. University of Padova. Available at: http://tesi.cab.unipd.it/63164/1/anna_ruggero_tesi.pdf (Accessed: 29.10.2022).
10. Goncalves M., Fox E., Watson L. Kipp N. (2004). Streams, Structures, Spaces, Scenarios, Societies (5S): A Formal Model for Digital Libraries. ACM Transactions on Information Systems, (2), pp. 270 – 312. Available at: http://ei.cs.vt.edu/~dlib/pdfs/5s5.pdf (Accessed: 29.10.2022).
11. Ferro N., Silvello G. (2013). NESTOR: A Formal Model for Digital Archives. Information Processing & Management, 49, pp. 1206 – 1240. Available at: http://www.dei.unipd.it/~ferro/papers/2013/IPM2013.pdf (Accessed: 29.10.2022).
12. Devezas J. (2021). Graph-Based Entity-Oriented Search. ACM SIGIR Forum, 55, pp. 29 – 79. Available at: https://repositorio-aberto.up.pt/bitstream/10216/133205/ 2/450176.pdf (Accessed: 29.10.2022).
13. Filimonov I. A. (2020). Experience in creating a personal bibliographic search system focused on a specific area of scientific or engineering knowledge. Trudy MAI, 114, pp. 1 – 35. Available at: http://mai.ru//upload/iblock/5a9/Filimonov_rus.pdf (Accessed: 29.10.2022). [in Russian language]
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15. Brack A., Hoppe A., Stocker M. (2020). Requirements Analysis for an Open Research Knowledge Graph. 24th International Conference on Theory and Practice of Digital Libraries, (1), pp. 3 – 18. Available at: https://arxiv.org/pdf/2005.10334.pdf (Accessed: 29.10.2022).
16. Wandmacher J. (2019). GOMS-Analysen MIT GOMSED. Technische Universität Darmstadt. Available at: https://www.researchgate.net/publication/267859320_GOMS-Analysen_mit_GOMSED (Accessed: 29.10.2022).
17. Khaet F., Alfimtsev A. (2017). The Extended Model of Goals, Operators, Methods and Selection Rules (GOMS) for Gesture Interfaces. Proceedings of the 13th Central & Eastern European Software Engineering Conference in Russia, (8), pp. 1 – 9. Available at: https://dl.acm.org/doi/10.1145/3166094.3166102 (Ac-cessed: 29.10.2022).
18. Jokinen J., Oulasvirta A., Howes A. (2022). Cognitive Modelling: From GOMS to Deep Reinforcement Learning. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 121, pp. 1 – 3. Available at: https://dl.acm.org/doi/10.1145/ 3491101.3503771 (Accessed: 29.10.2022).
19. Mishra W., Chowdhury A., Dhar D. (2017). Optimizing Operation Research Strategy for Design Intervention: A Framework for GOMS Selection Rule. International Conference on Research into Design, 65, pp. 61 – 70. Available at: https://link.springer.com/chapter/10.1007/978-981-10-3518-0_6 (Accessed: 29.10.2022).
20. Beckert B., Beuster G. (2006). A Method for Formalizing, Analyzing, and Verifying Secure User Interfaces. International Conference on Formal Engineering Methods, 4260, pp. 55 – 73. Available at: https://page-one.springer.com/pdf/preview/10.1007/ 11901433_4 (Accessed: 29.10.2022).
21. Nyström A. (2018). Gesture-Level Model: A modified Keystroke-Level Model for Tasks on Mobile Touchscreen Devices. Computer Science, (1), pp. 1 – 44. Available at: http://uu.diva-portal.org/smash/get/diva2:1235821/FULLTEXT01.pdf (Accessed: 29.10.2022).
22. Demchak B., Otasek D., Pico A., Bader D.et al. (2018). The Cytoscape Automation app article collection. Computer Science F1000 Research, (1), pp. 1 – 6. Available at: https://pdfs.semanticscholar.org/272f/ 482e15a7ec4eefd3576fe878e018422e24cd.pdf (Accessed: 29.10.2022).

Рус

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