Published June 30, 2021 | Version v1
Journal article Open

Devising a method to identify an incoming object based on the combination of unified information spaces

Creators

  • 1. National Technical University of Ukraine Kyiv Polytechnic Institute
  • 2. State University of Infrastructure and Technologies
  • 3. National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»
  • 4. V.E. Lashkaryov Institute of Semiconductor Physics NAS of Ukraine

Description

This paper suggests a method to search for an incoming object in order to identify its unambiguously, based on the integration of information spaces into intermediate unified information space. At the same time, the incoming object identification process involves appropriate attributes.

This paper describes the process of information object arrangement within a unified information space that forms for a set of dynamically changing objects. It should be noted that each subject in the set collects information about the environment, including interaction with other objects. In the process of forming a unified information space, the information system collects information from data sources that are represented in different formats. The system then converts this information and forms a unified information space, thereby providing users with information about objects.

A two-tier system of connections at the global (cloud) and local (fog) levels of interactions has been considered. At the same time, it should be noted that a unified information space formation requires the implementation of tools to support the transformation of information objects; that necessitates the implementation of translators ‒ special converters at different levels.

A method to combine information spaces into an intermediate unified information space has been proposed; analysis was performed to determine the time and efficiency of the search for incoming objects within it.

It was experimentally established that the more parameters that describe an information object, the less the time to identify an object depends on the length of the interval.

It has also been experimentally shown that the efficiency of finding incoming objects tends to be a directly proportional dependence while reducing the length of the interval and increasing the number of parameters, and vice versa

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References

  • Little, R. J., Rubin, D. B. (2019). Statistical analysis with missing data. John Wiley & Sons. doi: https://doi.org/10.1002/9781119482260
  • Zhu, L., Shi, C., Guo, J. (2015). Mapping discovery modeling and its empirical research for the scientific and technological knowledge concept in unified concept space. Cluster Computing, 18 (1), 103–112. doi: https://doi.org/10.1007/s10586-013-0339-7
  • Teraoka, T. (2012). Organization and exploration of heterogeneous personal data collected in daily life. Human-Centric Computing and Information Sciences, 2 (1), 1. doi: https://doi.org/10.1186/2192-1962-2-1
  • Cheng, T., Lauw, H. W., Paparizos, S. (2012). Entity Synonyms for Structured Web Search. IEEE Transactions on Knowledge and Data Engineering, 24 (10), 1862–1875. doi: https://doi.org/10.1109/tkde.2011.168
  • Yalova, K., Zavgorodnii, V., Romanyukha, M., Sorokina, L. (2016). Challenges and prospects in development of e-learning system for IT students. International Journal of Continuing Engineering Education and Life-Long Learning, 26 (1), 25. doi: https://doi.org/10.1504/ijceell.2016.075042
  • Shevchenko, I., Tertyshnyi, V., Koval, S. (2017). Designing a model of a decision support system based on a multi-aspect factographic search. Eastern-European Journal of Enterprise Technologies, 4 (2 (88)), 20–26. doi: https://doi.org/10.15587/1729-4061.2017.108569
  • Jáñez Morán, A., Profaizer, P., Herrando Zapater, M., Andérez Valdavida, M., Zabalza Bribián, I. (2016). Information and Communications Technologies (ICTs) for energy efficiency in buildings: Review and analysis of results from EU pilot projects. Energy and Buildings, 127, 128–137. doi: https://doi.org/10.1016/j.enbuild.2016.05.064
  • Yakovis, L. M. (2016). From unified information space to unified manufacturing control. Automation and Remote Control, 77 (9), 1689–1698. doi: https://doi.org/10.1134/s0005117916090150
  • Hu, Z., Mukhin, V., Kornaga, Y., Herasymenko, O., Mostoviy, Y. (2018). The Analytical Model for Distributed Computer System Parameters Control Based on Multi-factoring Estimations. Journal of Network and Systems Management, 27 (2), 351–365. doi: https://doi.org/10.1007/s10922-018-9468-x
  • Mukhin, V., Volokyta, A., Heriatovych, Y., Rehida, P. (2018). Method for Efficiency Increasing of Distributed Classification of the Images based on the Proactive Parallel Computing Approach. Advances in Electrical and Computer Engineering, 18 (2), 117–122. doi: https://doi.org/10.4316/aece.2018.02015
  • Teslia, I., Yehorchenkova, N., Khlevna, I., Kataieva, Y., Latysheva, T., Yehorchenkov, O. et. al. (2020). Developing a systems engineering concept for digitalizing higher education institutions. Eastern-European Journal of Enterprise Technologies, 6 (2 (108)), 6–20. doi: https://doi.org/10.15587/1729-4061.2020.219260
  • Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Biloshchytska, S., Kuzka, O., Shabala, Y., Lyashchenko, T. (2017). A method for the identification of scientists' research areas based on a cluster analysis of scientific publications. Eastern-European Journal of Enterprise Technologies, 5 (2 (89)), 4–11. doi: https://doi.org/10.15587/1729-4061.2017.112323
  • El Kadiri, S., Grabot, B., Thoben, K.-D., Hribernik, K., Emmanouilidis, C., von Cieminski, G., Kiritsis, D. (2016). Current trends on ICT technologies for enterprise information systems. Computers in Industry, 79, 14–33. doi: https://doi.org/10.1016/j.compind.2015.06.008
  • Arenas, M., Cuenca Grau, B., Kharlamov, E., Marciuška, Š., Zheleznyakov, D. (2016). Faceted search over RDF-based knowledge graphs. Journal of Web Semantics, 37-38, 55–74. doi: https://doi.org/10.1016/j.websem.2015.12.002
  • Arenas, M., Grau, B. C., Kharlamov, E., Marciuska, S., Zheleznyakov, D. (2014). Enabling Faceted Search over OWL 2 with SemFacet. OWLED, 121–132. Available at: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.660.9713&rep=rep1&type=pdf
  • Nagpal, G., Uddin, M., Kaur, A. (2012). A Comparative Study of Estimation by Analogy using Data Mining Techniques. Journal of Information Processing Systems, 8 (4), 621–652. doi: https://doi.org/10.3745/jips.2012.8.4.621
  • Mukhin, V., Komaga, Y., Zavgorodnii, V., Zavgorodnya, A., Herasymenko, O., Mukhin, O. (2019). Social Risk Assessment Mechanism Based on the Neural Networks. 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT). doi: https://doi.org/10.1109/atit49449.2019.9030519
  • Wang, T., Liu, L., Liu, N., Zhang, H., Zhang, L., Feng, S. (2020). A multi-label text classification method via dynamic semantic representation model and deep neural network. Applied Intelligence, 50 (8), 2339–2351. doi: https://doi.org/10.1007/s10489-020-01680-w
  • Pan, Y., Hu, G., Qiu, J., Zhang, Y., Wang, S., Shao, D., Pan, Z. (2020). FLGAI: a unified network embedding framework integrating multi-scale network structures and node attribute information. Applied Intelligence, 50 (11), 3976–3989. doi: https://doi.org/10.1007/s10489-020-01780-7
  • Yu, W., Cheng, S., Wu, C., Lou, H. (2012). A self-evolutionary model for automated innovation of construction technologies. Automation in Construction, 27, 78–88. doi: https://doi.org/10.1016/j.autcon.2012.04.018
  • Grau, B. C., Kharlamov, E., Marciuska, S., Zheleznyakov, D., Arenas, M. (2016). SemFacet: Faceted Search over Ontology Enhanced Knowledge Graphs. International Semantic Web Conference. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1068.337&rep=rep1&type=pdf
  • Ferré, S., Hermann, A. (2011). Semantic Search: Reconciling Expressive Querying and Exploratory Search. Lecture Notes in Computer Science, 177–192. doi: https://doi.org/10.1007/978-3-642-25073-6_12
  • Dodonov, A., Mukhin, V., Zavgorodnii, V., Kornaga, Ya., Zavgorodnya A. (2021). Method of searching for information objects in unified information space. System research and information technologies, 1, 34–46. doi: https://doi.org/10.20535/SRIT.2308-8893.2021.1.03
  • Ferré, S., Hermann, A., Ducassé, M. (2011). Semantic faceted search: Safe and expressive navigation in RDF graphs. HAL. Available at: https://hal.inria.fr/inria-00554093/document
  • Gershkovich, M. M., Birukova, T. K. (2014). The tasks of identification of informational objects in area-spread data arrays. Systems and Means of Informatics, 24 (1), 224–243. doi: https://doi.org/10.14357/08696527140114
  • Ozhereleva, T. А. (2014). Regard to the concept of information space, information field, information environment and semantic environment. International Journal of Applied and Fundamental Research, 10-2, 21–24. Available at: https://applied-research.ru/ru/article/view?id=5989
  • Karin, S. A. (2012). Integration in the Single Information Space of Heterogeneous Geospatial Data. Information and Control Systems, 2 (57), 89–94. Available at: http://www.i-us.ru/index.php/ius/article/view/13797
  • Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et. al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. doi: https://doi.org/10.15587/1729-4061.2020.208554
  • Logunov, A. N., Logunova, G. L. (2012). Choice of recognition attributes when searching hidden objects. Eastern-European Journal of Enterprise Technologies, 5 (9 (59)), 21–25. Available at: http://journals.uran.ua/eejet/article/view/4622
  • Meshcheryakov, R. V., Zhukovskiy, O. I., Senchenko, P. V., Gritsenko, Y. B., Milikhin, M. M. (2017). Architecture features of a common information space to manage complex technological processes. Proceedings of Tomsk State University of Control Systems and Radioelectronics, 20 (4), 75–81. doi: https://doi.org/10.21293/1818-0442-2017-20-4-75-81
  • Manakova, I. P. (2016). Sistema integratsii kontenta iz oblachnyh hranilisch i sotsial'nyh setey v edinoe informatsionnoe prostranstvo organizatsii. Nauchno-metodicheskiy elektronniy zhurnal Kontsept, 15, 476–480. Available at: https://e-koncept.ru/2016/86999.htm
  • Mukhin, V., Zavgorodnii, V., Barabash, O., Mykolaichuk, R., Kornaga, Y. et. al. (2020). Method of Restoring Parameters of Information Objects in a Unified Information Space Based on Computer Networks. International Journal of Computer Network and Information Security, 12 (2), 11–21. doi: https://doi.org/10.5815/ijcnis.2020.02.02
  • Sheludko, A. A., Boldyrikhin, N. V. (2018). Search of information objects in computer memory solving the problems of cyber security provision. Molodoy issledovatel' Dona, 6 (15), 81–86. Available at: https://cyberleninka.ru/article/n/poisk-informatsionnyh-obektov-v-pamyati-kompyutera-pri-reshenii-zadach-obespecheniya-kiberbezopasnosti