Authors:
Pierre Dagnely
;
Elena Tsiporkova
and
Tom Tourwé
Affiliation:
Sirris, Belgium
Keyword(s):
Event Relevancy Estimation, Data Reduction, Industrial Event Logs, Data Preprocessing.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial Applications of AI
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Visualization
Abstract:
With the realization of the industrial IoT, more and more industrial assets are continuously monitored by loggers
that report events (states, warnings and failures) occurring in or around these devices. Unfortunately, the
amount of events in these event logs prevent an efficient exploration, visualization and advanced exploitation
of this data. Therefore, a method that could estimate the relevancy of an event is crucial. In this paper, we
propose 10 methods, inspired from various research fields, to estimate event relevancy. These methods have
been benchmarked on two industrial datasets composed of event logs from two photovoltaic plants. We have
demonstrated that a combination of methods can detect irrelevant events (which can correspond to up to 90%
of the data). Hence, this is a promising preprocessing step that can help domain experts to explore the logs in
a more efficient way and can optimize the performance of analytical methods by reducing the training dataset
size w
ithout losing information.
(More)