Creating and sharing knowledge for telecommunications

Modelling patterns in continuous streams of data

Jesus, R. ; Antunes, M. ; Gomes, D.G. ; Aguiar, R.

Open Journal of Big Data Vol. 0, Nº 0, pp. 0 - 0, December, 2017.

ISSN (print): 2365-029X
ISSN (online):

Journal Impact Factor: 1,000 (in )

Digital Object Identifier:

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.