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Hierarchical Quasi-Neural Network Data Aggregation to Build a University Research and Innovation Management System

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International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies EMMFT 2019 (EMMFT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1259))

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Abstract

An approach to the formation of numerical values of metadata generated on the basis of the method of semantic decomposition of numerous indicators characterizing the research and innovation activities of the university, as well as their combination based on the method of hierarchical quasi-neural network aggregation, is considered. The developed methods are necessary for monitoring the state of scientific and innovative activities of the university, as a first step in building its management system. The aim of the work is to develop a method of hierarchical aggregation of data based on their presentation in the form of a quasi-neural network structure, the input of which is the data itself, and the output is a set of metadata or indicators of the state of the scientific and innovative activity of the university, characterizing the degree of data compliance with their planned criteria indicators. The leading approach includes: semantic decomposition of data into elementary aggregates; formation of aggregate metadata in the form of indicators characterizing the degree of compliance of aggregated data with planned criteria indicators.

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Authors and Affiliations

  1. Russian State Social University, Str. Wilhelm Pik, 4 Building 1, Moscow, 129226, Russia

    Andrey Krasnov & Svetlana Pivneva

Authors
  1. Andrey Krasnov
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  2. Svetlana Pivneva
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Corresponding authors

Correspondence to Andrey Krasnov or Svetlana Pivneva .

Editor information

Editors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic, Saint Petersburg, Russia

    Vera Murgul

  2. Saint Petersburg State University of Architecture and Civil Engineering, Saint Petersburg, Russia

    Viktor Pukhkal

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Krasnov, A., Pivneva, S. (2021). Hierarchical Quasi-Neural Network Data Aggregation to Build a University Research and Innovation Management System. In: Murgul, V., Pukhkal, V. (eds) International Scientific Conference Energy Management of Municipal Facilities and Sustainable Energy Technologies EMMFT 2019. EMMFT 2019. Advances in Intelligent Systems and Computing, vol 1259. Springer, Cham. https://doi.org/10.1007/978-3-030-57453-6_2

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