Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/21417
Title: | Extracting Knowledge from Stream Behavioural Patterns |
Author: | 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. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/21417 |
DOI: | 10.5220/0006373804190423 |
ISBN: | 978-989-758-245-5 |
Appears in Collections: | DETI - Comunicações IT - Comunicações |
Files in This Item:
File | Description | Size | Format | |
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IoTBDS.pdf | 106.05 kB | Adobe PDF | View/Open |
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