A Survey on Data Stream Mining Towards the Internet of Things Application

• Published in: 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) pp. 1 - 5
• Main Authors: Nahar, Saifun, Zhong, Ting, Monday, Happy N., Mills, Michael O., Nneji, Grace U., Abubakar, Hassan S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: Big Data Analytics; Data Processing Platforms; Deep Learning; Flowing Data Mining; internet of Things
• DOI: 10.1109/TIMES-iCON47539.2019.9024597

In the era of the Internet of Things, a widespread of applications depend on time with the various number of different data generated and collected from different devices available. These devices depend on the type of application. These fast stream data are real-time and large in dimension for the purpose of making decision as well as predicting future occurrence and analytics. Datastream analytics of internet technology for both businesses and everyday life is very valuable in terms of developing good quality of life. In this study, first of all, we focus on the concept of Internet of Things and its relationship with its architecture, large and flowing data. In addition, the approach of Internet of Things applied knowledge discovery process and deep learning frameworks are presented in this paper. Finally, the Internet of Things and its features are introduced in this work as well as the commonly used tools.