Data mining in IoT: data analysis for a new paradigm on the Internet

Wlodarczak, Peter and Ally, Mustafa and Soar, Jeffrey (2017) Data mining in IoT: data analysis for a new paradigm on the Internet. In: 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2017), 23-26 Aug 2017, Leipzig, Germany.

Abstract

This paper provides an overview on Data Mining (DM) technologies for the Internet of Things (IoT). IoT has become an active area of research, since IoT promises among other to improve quality of live and safety in Smart Cities, to make resource supply and waste management
more efficient, and optimize traffic. DM is highly domain
specific and depends on what is being mined for. For instance, if IoT is used to optimize traffic in a Smart City to reduce traffic jams and to find parking spaces quicker, different types of data needs
to be collected and analysed from an eHealth solution, where IoT is used in a Smart Home to monitor the well being of patients or elderly people. IoT connects things that can collect numeric data from smart sensors, streaming data from cameras or route information on maps. Depending on the type of data, different techniques need to be adopted to analyse them. Also, many IoT applications analyse data from different devices and correlate them to make predictions about possible machine failures in production sites or
looming emergency situations in Smart Buildings in a home security application. DM techniques need to handle the heterogeneity of IoT data, the large volumes of data and the speed at which they are produced. This paper explores the state of the art DM techniques for IoT.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright © 2017 by the Association for Computing Machinery, Inc. Permanent restricted access to PV in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 25 Oct 2017 04:37
Last Modified: 19 Sep 2018 03:25
Uncontrolled Keywords: Internet of Things, data mining, machine learning, predictive analytics, Smart City
Fields of Research : 08 Information and Computing Sciences > 0805 Distributed Computing > 080503 Networking and Communications
Identification Number or DOI: 10.1145/3106426.3115866
URI: http://eprints.usq.edu.au/id/eprint/33255

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