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Lookup NU author(s): Dr Jonathan Law, Professor Darren Wilkinson
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Data is rapidly increasing in volume and velocityand the Internet of Things (IoT) is one importantsource of this data. The IoT is a collection of connecteddevices (things) which are constantly recordingdata from their surroundings using on-board sensors.These devices can record and stream data to thecloud at a very high rate, leading to high storage andanalysis costs. In order to ameliorate these costs, thedata is modelled as a stream and analysed online tolearn about the underlying process, perform interpolationand smoothing and make forecasts and predictions.Conventional state space modelling tools assumethe observations occur on a fixed regular time grid.However, many sensors change their sampling frequency,sometimes adaptively, or get interrupted and re-startedout of sync with the previous sampling grid, or justgenerate event data at irregular times. It is thereforedesirable to model the system as a partially and irregularlyobserved Markov process which evolves incontinuous time. Both the process and the observationmodel are potentially non-linear. Particle filters thereforerepresent the simplest approach to online analysis.A functional Scala library of composable continuoustime Markov process models has been developed in orderto model the wide variety of data captured in theIoT.
Author(s): Law J, Wilkinson DJ
Publication type: Article
Publication status: Published
Journal: Statistics and Computing
Year: 2018
Volume: 28
Issue: 6
Pages: 1119-1137
Print publication date: 01/11/2018
Online publication date: 31/10/2017
Acceptance date: 22/09/2017
Date deposited: 22/09/2017
ISSN (print): 0960-3174
ISSN (electronic): 1573-1375
Publisher: Springer
URL: https://doi.org/10.1007/s11222-017-9783-1
DOI: 10.1007/s11222-017-9783-1
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