Big Data incorporation based on Open Services Provider for distributed enterprises

Surnin, Oleg; Sitnikov, Pavel; Ivaschenko, Anton; Ilyasova, Nataly; Popov, Sergey Borisovich

doi:10.18287/1613-0073-2017-1903-42-47

Cited by 4 publications

(3 citation statements)

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“…Based on the analysis of the state-of-the-art and some experience in the area of semantic analysis and enterprise content management [23][24][25], we have identified a gap in intelligent text understanding and text generation application to practice. This gap consists of the lack of adaptive software architecture capable of combination and extension of artificial intelligent solutions according to the developing situations and changing problem statements, without retraining of the neural networks.…”

Section: State-of-the-artmentioning

confidence: 99%

Hybridization of Intelligent Solutions Architecture for Text Understanding and Text Generation

et al. 2021

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This study proposes a new logical model for intelligent software architecture devoted to improving the efficiency of automated text understanding and text generation in industrial applications. The presented approach introduces a few patterns that provide a possibility to build adaptable and extensible solutions using machine learning technologies. The main idea is formalized by the concept of expounder hybridization. It summarizes an experience of document analysis and generation solutions development and social media analysis based on artificial neural networks’ practical use. The results of solving the task by the best expounder were improved using the method of aggregating multiple expounders. The quality of expounders’ combination can be further improved by introducing the pro-active competition between them on the basis of, e.g., auctioning algorithm, using several parameters including precision, solution performance and score. Analysis of the proposed approach was carried out using a dataset of legal documents including joint-stock company decision record sheets and protocols. The solution is implemented in an enterprise content management system and illustrated by an example of processing of legal documentation.

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Section: State-of-the-artmentioning

confidence: 99%

Hybridization of Intelligent Solutions Architecture for Text Understanding and Text Generation

et al. 2021

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“…During the development, a significant increase in key performance indicators is expected, according to the McKinsey Digital Economy Report [1]:…”

Section: Technology Reviewmentioning

confidence: 99%

“…The challenges are related to the requirement to use unified and integrated software services based on a reliable platform, instead of developing individual algorithms and software solutions. To cover this gap in this article, it is proposed to summarize existing experience in this area [1,2] and develop an architectural solution for a software development kit that can be used as a reliable platform for building industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Industrial application of big data services in digital economy

Surnin¹,

Code”²,

Sitnikov

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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Nowadays, the world is moving to automation. Appropriate programs for the implementation of industrial applications are developed by many companies. But is it so easy to implement systems capable of processing large amounts of information in production? Despite multiple positive results in research and development of Big Data technologies, their practical implementation and use remain challenging. At the same time most prominent trends of digital economy require Big Data analysis in various problem domains. We carried out the analysis of existing data processing works. Based on generalization of theoretical research and a number of real economy projects in this area there is proposed in this paper an architecture of a software development kit that can be used as a solid platform to build industrial applications. Was formed a basic algorithm for processing data from various sources (sensors, corporate systems, etc.). Examples are given for automobile industry with a reference of Industry 4.0 paradigm implementation in practice. The given examples are illustrated by trends graphs and by subject area ontology of the automotive industry. Data Science O L Surnin, P V Sitnikov , A A Khorina, A V Ivaschenko, A A Stolbova and N Yu Ilyasova V International Conference on "Information Technology and Nanotechnology" (ITNT-2019) 410 Data Science O L Surnin, P V Sitnikov , A A Khorina, A V Ivaschenko, A A Stolbova and N Yu Ilyasova

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