Using Semantic Modelling to Improve the Processing Efficiency of Big Data in the Internet of Things Domain
Anatoly Ya. Gladun, Juliia V. Rogushina, Aliaksei Andrushevich · Cybernetics and computer engineering · 2019
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. 27