Abstract
The current challenges of decision support systems require a complex analysis of heterogeneous data. These include social and technical information and have various formats. In addition, this information is often incomplete about the domain. Data parts belong to different domains. Such information is defined in this paper as heterogeneous semi-structured objects. The author offers an approach to formalization and comparison of such data sets based on object model and vectorization. The novelty of the work lies in the object similarity measure. One can match objects of any type between themselves in the conditions of information incompleteness. The paper describes a method of formalizing data, matching method, advantages and disadvantages of the proposed solutions. As an example, the authors consider the application of the method in the data analysis of the solving of information security problems. In the paper, the system architecture of the decision support system based on the obtained results is presented.
Project is financially supported by the Ministry of Education and Science of the Russian Federation, Federal Program “Research and Development in Priority Areas of Scientific and Technological Sphere in Russia for 2014–2020” (Contract No. 14.578.21.0231; September 26, 2017, the unique identifier of agreement RFMEFI57817X0231).
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Poltavtseva, M., Zegzhda, P. (2019). Heterogeneous Semi-structured Objects Analysis. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_88
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DOI: https://doi.org/10.1007/978-3-030-01054-6_88
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