Publication
IOTSTREAMING 2017
Conference paper

A sliding window filter for time series streams

Abstract

The ever increasing number of sensor-equipped devices comes along with a growing need for data analysis techniques that are able to process time series streams in an online fashion. Although many sensor-equipped devices produce never-ending data streams, most real-world applications merely require high-level information about the presence or absence of certain events that correspond to temporal patterns. Since online event detection is usually computational demanding, we propose a sliding window filter that decreases the time/space complexity and, therefore, allows edge computing on devices with only few resources. Our evaluation for online gesture recognition shows that the developed filtering approach does not only reduce the number of expensive dissimilarity comparison, but also maintains high precision.

Date

18 Sep 2017

Publication

IOTSTREAMING 2017

Authors

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