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 Extracting Knowledge from Stream Behavioural Patterns
Please use this identifier to cite or link to this item http://hdl.handle.net/10773/21417

title: Extracting Knowledge from Stream Behavioural Patterns
authors: Jesus, Ricardo
Antunes, Mário
Gomes, Diogo
Aguiar, Rui
Aguiar, Rui
keywords: Stream Mining
IoT
Machine Learning
Context Awareness
M2M
issue date: 2017
publisher: SCITEPRESS - Science and Technology Publications
abstract: The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area (Antunes et al., 2016b). With this in mind we propose a stream characterization model which aims to provide the foundations of a new stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic organizational model without enforcing specific representations.
URI: http://hdl.handle.net/10773/21417
ISBN: 978-989-758-245-5
publisher version/DOI: https://doi.org/10.5220/0006373804190423
source: 2nd International Conference on Internet of Things, Big Data and Security
appears in collectionsDETI - Comunicações

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