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A usage analytics model for analysing user behaviour in IBM academic cloud

Kesavulu, Manoj orcid logoORCID: 0000-0001-5505-9593, Dang-Nguyen, Duc-Tien orcid logoORCID: 0000-0002-2761-2213, Bezbradica, Marija orcid logoORCID: 0000-0001-9366-5113 and Helfert, Markus orcid logoORCID: 0000-0001-6546-6408 (2018) A usage analytics model for analysing user behaviour in IBM academic cloud. In: 6th International IBM Cloud Academy Conference ICACON 2018, 24-26 May, 2018, Tokyo, Japan.

Abstract
Usage in the software domain refers to the knowledge about how end- users use the application and how the application responds to the user’s actions. Usage can be revealed by monitoring the user’s interaction with the application. However, in the cloud environment, it is non-trivial to understand the interactions of the users by using only the monitoring solutions. For example, user’s behaviour, user’s usage pattern, which features of a cloud application are critical for a user, to name a few, cannot be extracted using only the existing monitoring tools. Understanding these information require additional analysis, which can be done by using usage analytics. For this purpose, in this paper, we propose a novel process model design for incorporating Usage Analytics in a cloud environment. We evaluate this proposed process model in the context of academic applications and services in the cloud, with the focus on IBM Academic Cloud.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Usage; Analytics; IBM Academic Cloud; Process Model; End-user; Application; Cloud; Agile; Software
Subjects:Computer Science > Computer software
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > Lero: The Irish Software Engineering Research Centre
Research Institutes and Centres > ADAPT
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland grant 13/RC/2094, European Regional Development Fund through the Southern & Eastern Regional Operational Programme
ID Code:22446
Deposited On:03 Jul 2018 11:54 by Duc-Tien Dang-Nguyen . Last Modified 28 Mar 2022 12:02
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