@inproceedings{Derguech2014a,
abstract = {Public datasets are becoming more and more avail- able for organizations. Both public and private data can be used to drive innovations and new solutions to various problems. The Internet of Things (IoT) and Open Data are particularly promising in real time predictive data analytics for effective decision support. The main challenge in this context is the dynamic selection of open data and IoT sources to support predictive analytics. This issue is widely discussed in various domains including economics, market analysis, energy usage, etc. Our case study is the prediction of energy usage of a building using open data and IoT. We propose a two-step solution: (1) data management: collection, filtering and warehousing and (2) data analytics: source selection and prediction. This work has been evaluated in real settings using IoT sensors and open weather data.},
author = {Derguech, Wassim and Bruke, Eanna and Curry, Edward},
booktitle = {2014 IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing},
doi = {10.1109/UIC-ATC-ScalCom.2014.137},
file = {:Users/ed/Library/Application Support/Mendeley Desktop/Downloaded/Derguech, Bruke, Curry - 2014 - An Autonomic Approach to Real-Time Predictive Analytics Using Open Data and Internet of Things.pdf:pdf},
isbn = {978-1-4799-7646-1},
keywords = {Autonomic System,Energy Management,IoT,Machine Learning,Open Data,Predictive Analytics},
month = {dec},
pages = {204--211},
publisher = {IEEE},
title = {{An Autonomic Approach to Real-Time Predictive Analytics Using Open Data and Internet of Things}},
url = {http://www.edwardcurry.org/publications/Wassim_UIC2014.pdf},
year = {2014}
}