Accepted version, 929.0Kb
Garae, J., Ko, R. K. L., & Chaisiri, S. (2016). UVisP: User-centric visualization of data provenance with gestalt principles. In Proceedings of 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, August 23-26, 2016, Tianjin, China (pp. 1923–1930). Washington, DC, USA: IEEE Computer Society. https://doi.org/10.1109/TrustCom.2016.0294
Permanent Research Commons link: https://hdl.handle.net/10289/10996
The need to understand and track files (and inherently, data) in cloud computing systems is in high demand. Over the past years, the use of logs and data representation using graphs have become the main method for tracking and relating information to the cloud users. While being used, tracking related information with 'data provenance' (i.e. series of chronicles and the derivation history of data on metadata) is the new trend for cloud users. However, there is still much room for improving data activity representation in cloud systems for end-users. We propose 'User-centric Visualization of data provenance with Gestalt (UVisP)', a novel user-centric visualization technique for data provenance. This technique aims to facilitate the missing link between data movements in cloud computing environments and the end-users uncertain queries over their files security and life cycle within cloud systems. The proof of concept for the UVisP technique integrates an open-source visualization API with Gestalt's theory of perception to provide a range of user-centric provenance visualizations. UVisP allows users to transform and visualize provenance (logs) with implicit prior knowledge of 'Gestalt's theory of perception.' We presented the initial development of the UVisP technique and our results show that the integration of Gestalt and 'perceptual key(s)' in provenance visualization allows end-users to enhance their visualizing capabilities, to extract useful knowledge and understand the visualizations better.
IEEE Computer Society
This is an author’s accepted version of an article published in the Proceedings of 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications. ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.