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Title: Immersive social visual analytics
Author: Kavuncu, Ahmet Bertan
ISNI:       0000 0004 9356 9860
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2019
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Increase in data availability in the last decades started a new multi-disciplinary and data-driven approach to be taken in research projects. This new approach requires a matching set of data analytics methods that can target the broadness of data and mixture of expertise in those research projects. We believe that Large High Resolution Display (LHRD) based Immersive and Social Visual Data Analytics system is the solution. LHRDs are large scale display walls that are built for research purposes, which are ideal for conducting the visual data analytics these multi-disciplinary data-driven research approach requires, by providing unique properties in terms of immersion, collaboration, visualisation and interactivity. This thesis surveyed existing software in the literature for LHRDs and identified many challenges that needed to be solved, in order to conduct visual data analytics for this objective. Hence, this thesis introduces and demonstrates its novel Clustered Visualisation Services Paradigm (CVSP) in its Data Observatory Software Framework (GDO) for conducting Immersive and Social Visual Data Analytics on LHRDs. This paradigm enables system and visualisation app logics to have components on distributed locations of LHRD, where these components communicate, coordinate and form functionality through structured communication using APIs. Through this paradigm, we enable implementation of immersive and scalable display of versatile, modular, general, interactive, data-driven data visualisation applications and we also enable management of them through our underlying operating system. Furthermore, this paradigm enables collaging and syncing of applications for creating coordinated data visualisation scenes composed of multi-view visualisations that can implement complex interaction scenarios on LHRDs. In summary, this thesis presents architecture and implementation of Data Observatory Software Framework (GDO), which is used at KPMG Data Observatory, Data Science Institute, Imperial College London; its novel software paradigm: Clustered Visualisation Services Paradigm (CVSP) and many novel or critical functionality provided by this paradigm that overcomes challenges for conducting Immersive and Social Visual Data Analytics for multi-disciplinary data-driven research projects.
Supervisor: Guo, Yi-ke Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral