Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/19632
Title: Dark data in internet of things (IOT): challenges and opportunities
Authors: Zdraveski, Vladimir 
Trajanov, Dimitar 
Stojanov, Riste 
Kocarev, Ljupco
Keywords: Dark data, Internet of Things (IoT), Machine Learning, Data Science
Issue Date: Feb-2018
Conference: Proceedings of the 7th Small Systems Simulation Symposium 2018, Nish, Serbia
Abstract: Nowadays we are witnessing the establishment of the data-driven science as a new scientific paradigm, that is opening a waste amount of new opportunities for scientific and technological advances. The data is becoming the main asset in today’s science and technology. Unfortunately, a significant amount of available and stored data is not used today. This data is known as a dark data. Starting from this point, the primary goal of this paper is to raise the awareness of the opportunities that are explored with the dark data utilization in companies and organizations, by giving an overview of the underlining technologies, proposing a methodology and showing example projects that utilize the dark data in the IoT domain.
URI: http://hdl.handle.net/20.500.12188/19632
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers

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