Towards a Semantic Modelling of Profiling Data in Industry 4.0

Tracking #: 2223-3436

This paper is currently under review
Authors: 
Imran Jami
Shaukat Wasi
Siraj Munir

Responsible editor: 
Guest Editors SemWeb of Things for Industry 4.0 - 2019

Submission type: 
Full Paper
Abstract: 
In this paper we present a framework for semantic representation of profiling information of workers and employees in Industry 4.0 setting using the IoT devices. With the integration of Internet of Things with Big Data and Semantic Web, we provide effective retrieval of profiling information of actors in real-time within the industry. It aims at supporting intelligent queries about the workers working in the Industry by using temporal knowledge graph generated from the multi attributed logs identified from the surveillance video. The paper facilitates integration of the profiling knowledge graph with existing industry knowledge graph for unified view of profiling log with workers information. Decentralized decision making in real time can be made with this system which is one of the core areas of Industry 4.0. Knowledge Graph is proposed as generic for reusability in any industries and enterprise for profiling purpose. The results show the highlights of the queries about working in the industry without the involvement of any resource. The paper is concluded with shortcomings in this work along with the solution.
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Under Review