Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/21336
Title: | Modelling patterns in continuous streams of data |
Author: | Jesus, R Antunes, M Gomes, D Aguiar, R |
Keywords: | Stream Mining Time Series Machine Learning IoT M2M |
Issue Date: | 2017 |
Publisher: | Research Online Publishing |
Abstract: | The untapped source of information, extracted from the increasing number of sensors, can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. The lack of a standard context representation scheme is one of the main struggles in this research area, conventional methods for extracting knowledge from data rely on a standard representation or a priori relation. Which may not be feasible for IoT and M2M scenarios, with this in mind we propose a stream characterization model which aims to provide the foundations for a novel stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic stream organizational model without enforcing specific representations. In this paper we extend our work on stream characterization and devise a novel similarity method |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/21336 |
ISSN: | 2365-029X |
Appears in Collections: | DETI - Artigos IT - Artigos |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.