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Big Data for Monitoring Mobile Applications

Authors:
Fabrice Mourlin
Djiken Guy Lahlou
Laurent Nel

Keywords: Big Data; indexing; log analysis; distributed application; AI model; storage server; anomaly detection.

Abstract:
The behavior monitoring is an activity that cuts across all software. It ensures that the application proceeds as expected. Our Big Data workflow supports data streams of varying rates. The collected data sometimes contains errors that we want to explain. To this end, we seek to trace the important events of our calculations in order to qualify the anomalies in our processing and then easily trace the origin of the problem. We have implemented a monitoring layer within mobile applications in order to perform smart control over a set of mobile devices. We have defined a Big Data workflow to collect, index and store the log data in order to submit it to an Artificial Intelligence (AI) model. We detect behavioural anomalies through the analysis of software logs deployed on embedded devices. Based on the patterns recognised in the logs, our AI model provides us with a sequence of system operations. These operations are scheduled to re-deploy a service, change a driver, perform a library update, etc. The critical points concern the management of the Android APIs with respect to the deployed software; we must manage with precision the software updates with respect to the firmware versions among other things. In the end, management reports are built every week and issued to the maintenance team. These documents are the record of maintenance activities. They provide an explanation for periods of non-availability of equipment and for the withdrawal of obsolete equipment.

Pages: 15 to 23

Copyright: Copyright (c) IARIA, 2022

Publication date: April 24, 2022

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-61208-945-4

Location: Barcelona, Spain

Dates: from April 24, 2022 to April 28, 2022