Abstract
Innovations are the key factor of the competitiveness of any modern business. This paper gives the systematized results of investigations on the data warehouse technology with an automatic data-replenishment from heterogeneous sources. The data warehouse is suggested to contain information about objects having a significant innovative potential. The selection mechanism for such information is based on quantitative evaluation of the objects innovativeness, in particular their technological novelty and relevance for them. The article presents the general architecture of the data warehouse, describes innovativeness indicators, considers Theory of Evidence application for processing incomplete and fuzzy information, defines basic ideas of measurement processing procedure to compute probabilistic values of innovativeness components, summarizes using evolutional approach in forming the linguistic model of object archetype, gives information about an experimental check if the model developed is adequate. The results of these investigations can be used for business planning, forecasting technological development, investment project expertise.
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Acknowledgements
This work was done at the Tver State Technical University with supporting of the Russian Foundation of Basic Research (projects No. 18-07-00358 and No. 17-07-01339) and at the Joint Supercomputer Center of the Russian Academy of Sciences – Branch of NIISI RAS within the framework of the State assignment (research topic 065-2019-0014).
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Ivanov, V.K., Palyukh, B.V., Sotnikov, A.N. (2020). Features of Data Warehouse Support Based on a Search Agent and an Evolutionary Model for Innovation Information Selection. In: Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19). IITI 2019. Advances in Intelligent Systems and Computing, vol 1156. Springer, Cham. https://doi.org/10.1007/978-3-030-50097-9_13
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