DOI:https://doi.org/10.15407/kvt196.02.027
Cybernetics and Computer Engineering, 2019, 2 (196), pp. 27-42
A.Ya. Gladun1, PhD (Engineering),
Senior Researcher of the Department of Complex Research of Information Technologies and Systems
email: glanat@yahoo.com
Yu.V. Rogushina2, PhD (Phys&Math)
Senior Researcher of the Department of Automated Information Systems
ladanandraka2010@gmail.com
A.A. Andrushevich3, Researcher
of the Faculty of Apply Mathematics and Сomputer Science
email: andrushevich@bsu.by
1International Research and Training
Center for Information Technologies and Systems
of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine
40, Acad. Glushkov av., 03187, Kiev, Ukraine
2Institute of Program Systems
of the National Academy of Sciences of Ukraine,
40, Acad. Glushkov av., 03187, Kiev, Ukraine
3Belarusian State University,
4, Nezavisimosti av., 220030, Minsk, Belarus
USING SEMANTIC MODELING TO IMPROVE THE PROCESSING EFFICIENCY OF BIG DATA IN THE INTERNET OF THINGS DOMAIN
Introduction. The development of the Internet of Things (IoT), equipped with various electronic sensors and controllers that distantly operate with these things is an important step of a new technical revolution. In this article, we look at the features of Big Data generated by the Internet of Things (IoT) technology, and also present the methodology for processing this Big Data with use of semantic modeling (ontologies) at all stages of the Big Data life cycle. Semantic modeling allows to eliminate such contradictions in these technologies as the heterogeneity of devices and things that causes the heterogeneity of the data types produced by them. Machine learning is used as an instrument for Big Data of analyzes: it provides logical inference of the rules that can be applied to processing of information generated by Smart Home system.
The purpose of the article is to use deep machine learning, based on convolutional neural networks because this model of machine learning corresponds to processing of unstructured and complex nature of the IoT domain.
Results. Proposed approach increases the efficiency of IoT Big Data processing and differs from traditional processing systems by using NoSQL database, distributed architectures and semantic modeling.
Conclusion. The conceptual architecture of the Big Data processing system for IoT and describe it on on the example of the NoSQL database for Smart Home were given. This architecture consists of five independent levels. At each of these levels, a combined approach of semantic modeling and data mining methods can be used. Currently, this platform can be combined with a lot of open source components.
Keywords: Big Data, Internet of Things, ontology, Semantic Web.
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Received 10.01.2019