@inproceedings{V.K.2020,

title = {Features of Data Warehouse Support Based on a Search Agent and an Evolutionary Model for Innovation Information Selection},

author = {Ivanov V.K. and Palyukh B.V. and Sotnikov A.N.},

url = {https://disk.yandex.ru/i/FT7JLsQmXPIMgQ

https://doi.org/10.1007/978-3-030-50097-9_13},

doi = {10.1007/978-3-030-50097-9_13},

isbn = {978-30-3050-096-2},

year  = {2020},

date = {2020-00-01},

urldate = {2020-00-01},

booktitle = {Advances in Intelligent Systems and Computing. Proceedings of the Fourth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’19) },

volume = {1156},

pages = {120-130},

publisher = {Springer, Cham},

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.



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 (Scopus)},

keywords = {data warehouse, genetic algorithm, innovation index, Innovativeness, Intelligent agent, novelty, relevance, subject search},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {Computational Model to Quantify Object Innovativeness},

author = {Ivanov V.K.},

url = {https://disk.yandex.ru/i/wHOy6DTBYeabog},

issn = {1613-0073},

year  = {2018},

date = {2018-10-31},

urldate = {2022-08-29},

booktitle = {Proceedings of the II International Scientific and Practical Conference “Fuzzy Technologies in the Industry – FTI 2018” },

volume = {2258},

pages = {249-258},

publisher = {Ulyanovsk},

abstract = {The article considers the quantitative assessment approach to the innovativeness of different objects. The proposed assessment model is based on the object data retrieval from various databases including the Internet. We present an object linguistic model, the processing technique for the measurement results including the results retrieved from the different search engines, and the evaluating technique of the source credibility. Empirical research of the computational model adequacy includes the acquisition and preprocessing of patent data from different databases and the computation of invention innovativeness values: their novelty and relevance. The experiment results, namely the comparative assessments of innovativeness values and major trends, show the models developed are sufficiently adequate and can be used in further research.},

keywords = {assessment, innovation, innovation index, Innovativeness, linguistic model, novelty, relevance},

pubstate = {published},

tppubtype = {inproceedings}

}