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Details of Grant 

EPSRC Reference: EP/M008959/2
Title: Visualization and Data Analysis at the Big Data Scale
Principal Investigator: Borgo, Dr R
Other Investigators:
Researcher Co-Investigators:
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Department: Informatics
Organisation: Kings College London
Scheme: First Grant - Revised 2009
Starts: 28 November 2016 Ends: 27 July 2017 Value (£): 25,919
EPSRC Research Topic Classifications:
Computer Graphics & Visual.
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
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Panel History:  
Summary on Grant Application Form
Studies show that visual stimuli such as images are processed by the brain 60,000 faster than text. When visuals are used to represent information the specific choice of representation will greatly influence the insights that can be gained from the displayed information and, as a consequence, greatly affect our decision-making.

Appropriate use of visuals and exploitation of their effectiveness therefore plays a majour role at any level of the society.

Visualization has been shown very effective in understanding large, complex data, and thus has become an indispensable tool for many areas of research and practice.

With the current era of data deluge, also known as Big Data, the creation of highly expressive visual encoding of information is therefore critical to be able to support the process of analysis, exploration, and presentation of data for knowledge discovery and decision making.

Development of new and improved visualization methods for managing content and structure of large dataset is therefore a topical research focus; one needed to effectively make sense and maximize utilization of such vast amounts of data, there is a need of new sets of tools beyond conventional techniques, tools capable of leveraging the power of standard data analysis techniques such as data mining and statistical analysis.

In this project we propose glyph based visual encoding as a possible solution to tackle the challenge of Big Data analysis.

Glyph-based visualization is a common form of visual design where a data set is depicted by a collection of visual objects referred to as glyphs, representations conceptually similar to icons or signs.

Glyph-based visualization is still an under-explored field with great potentials, therefore, we aim at developing a comprehensive framework for the classification and deployment of glyph-based visual designs in the context of large datasets (e.g. Big Data). Fundamental to our research will be the application of graphics and perception rules to the process of associating data, and information they carry, to visual encodings and visual encodings to analytical tasks.

Visual perception plays an important role in the area of visualization, appropriate application of perception principles can significantly improve both the quality and the quantity of information being displayed, and directly impact a viewer's efficiency and effectiveness while performing analytical tasks such as: target search, pattern recognition, anomalies or recurring event detection.



In the data deluge of the modern era, interdisciplinary collaboration is also an absolute necessity for identification, extraction and visualization of pertinent data. By creating context sensitive tools, it is possible to significantly expedite the analytical process with solutions optimized for individuals knowledge base and expertise. For this reason we aim to validate and refine results at each step of our work with real case studies scenarios. We will be focusing in particular to datasets generated by Lattice Quantum Chromodynamics (QCD) simulations. Lattice QCD simulations are pivotal to solve fundamental problems in high energy and nuclear physics such as the understanding of internal structure of nucleons, determine the properties of strongly interacting matter under extreme conditions, such as those that existed immediately after the "Big Bang" and are reproduced today in relativistic simulation experiments.

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