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Understanding User Behaviour through Action Sequences: from the Usual to the Unusual

Nguyen, P. ORCID: 0000-0001-5643-0585, Turkay, C. ORCID: 0000-0001-6788-251X, Andrienko, G. ORCID: 0000-0002-8574-6295 , Andrienko, N. ORCID: 0000-0003-3313-1560, Thonnard, O. & Zouaoui, J. (2018). Understanding User Behaviour through Action Sequences: from the Usual to the Unusual. IEEE Transactions on Visualization and Computer Graphics, 25(9), pp. 2838-2852. doi: 10.1109/tvcg.2018.2859969


Action sequences, where atomic user actions are represented in a labelled, timestamped form, are becoming a fundamental data asset in the inspection and monitoring of user behaviour in digital systems. Although the analysis of such sequences is highly critical to the investigation of activities in cyber security applications, existing solutions fail to provide a comprehensive understanding due to the complex semantic and temporal characteristics of these data. This paper presents a visual analytics approach that aims to facilitate a user-involved, multi-faceted decision making process during the identification and the investigation of “unusual” action sequences. We first report the results of the task analysis and domain characterisation process. Then we describe the components of our multi-level analysis approach that comprises of constraint-based sequential pattern mining and semantic distance based clustering, and multi-scalar visualisations of users and their sequences. Finally, we demonstrate the applicability of our approach through a case study that involves tasks requiring effective decision-making by a group of domain experts. Although our solution here is tightly informed by a user-centred, domain-focused design process, we present findings and techniques that are transferable to other applications where the analysis of such sequences is of interest.

Publication Type: Article
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: action sequence, event sequence, sequential pattern mining, visual analytics, cyber security, user behaviour
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
School of Science & Technology > Computer Science > giCentre
SWORD Depositor:
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