Modelling patterns in continuous streams of data

• Author(s): Jesus, R ; Antunes, M ; Gomes, D ; Aguiar, R
• Date: 2017
• Persistent ID: http://hdl.handle.net/10773/21336
• Origin: RIA - Repositório Institucional da Universidade de Aveiro
• Subject(s): Stream Mining; Time Series; Machine Learning; IoT; M2M
• Document Type: Journal article
• Language: English

Description

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