Title: Grouping visual enhancements for Picviz logging visualisation

Authors: Yue Yang; Shraddha Sen; Yang Xiao; Tieshan Li

Addresses: Navigation College, Dalian Maritime University, Dalian, China ' Department of Computer Science, The University of Alabama Tuscaloosa, AL, 35487-0290, USA ' Department of Computer Science, The University of Alabama Tuscaloosa, AL, 35487-0290, USA ' Navigation College, Dalian Maritime University, Dalian, China; School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

Abstract: The logs generated during various processes, such as networking and web surfing, can be voluminous. These logs need to be processed and analysed to enhance the system's quality and performance and facilitate proactive fault detection and handling. Log analysis software such as Picviz, which was built to show huge volumes of data for the sake of security, is one example. The parallel coordinate system in Picviz allows data to be presented in multiple dimensions. Its main aim is to simplify data analysis and identify correlations among variables. However, representing a large amount of data all at once in the software can lead to congested and clustered lines, making it challenging to distinguish and extract information. To address this issue, we propose two improved methods: 'grouping based on comparison of data' and 'grouping of consecutive data'. In order to simplify the reading experience, these plug-ins collect related lines into sets and present them all at once, making the image more readable and comprehensible. This paper outlines four cases to highlight these plug-ins' significance and potential application scenarios for each case.

Keywords: data visualisation; sensor data visualisation; logging; security; software; security.

DOI: 10.1504/IJSNET.2023.131640

International Journal of Sensor Networks, 2023 Vol.42 No.2, pp.102 - 112

Received: 13 May 2022
Accepted: 23 Feb 2023
Published online: 21 Jun 2023 *

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