Predictive Analytics for Complex IoT Data Streams

Akbar, Adnan, Khan, Abdullah, Carrez, Francois and Moessner, Klaus (2017) Predictive Analytics for Complex IoT Data Streams IEEE Internet of Things, 4 (5). pp. 1571-1582.

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Abstract

The requirements of analyzing heterogeneous data streams and detecting complex patterns in near real-time have raised the prospect of Complex Event Processing (CEP) for many internet of things (IoT) applications. Although CEP provides a scalable and distributed solution for analyzing complex data streams on the fly, it is designed for reactive applications as CEP acts on near real-time data and does not exploit historical data. In this regard, we propose a proactive architecture which exploits historical data using machine learning (ML) for prediction in conjunction with CEP. We propose an adaptive prediction algorithm called Adaptive Moving Window Regression (AMWR) for dynamic IoT data and evaluated it using a real-world use case with an accuracy of over 96%. It can perform accurate predictions in near real-time due to reduced complexity and can work along CEP in our architecture. We implemented our proposed architecture using open source components which are optimized for big data applications and validated it on a use-case from Intelligent Transportation Systems (ITS). Our proposed architecture is reliable and can be used across different fields in order to predict complex events.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Akbar, Adnan adnan.akbar@surrey.ac.uk Khan, Abdullah Carrez, Francois F.Carrez@surrey.ac.uk Moessner, Klaus K.Moessner@surrey.ac.uk
Date :	6 June 2017
DOI :	10.1109/JIOT.2017.2712672
Copyright Disclaimer :	Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords :	Complex event processing, data streams, internet of things, machine learning, predictive analytics, proactive applications, regression, time series prediction
Depositing User :	Melanie Hughes
Date Deposited :	02 Jun 2017 09:52
Last Modified :	14 Nov 2018 10:51
URI:	http://epubs.surrey.ac.uk/id/eprint/841273

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