Personalized Hierarchical Visualization for Enhanced Decision Support using Big Data
Lead Research Organisation:
Newcastle University
Department Name: Computing Sciences
Abstract
Background:
Real time data analytics and its visualization are increasingly important in today's world of data. Current IDC reports state that by 2020, 50% of all business analytics software will incorporate prescriptive analytics built on cognitive computing functionality. It also states that big data-related professional services will have a compound annual growth rate of 23% by 2020. An article on EMC digital universe predicts digital data will increase up to 40% by the next decade, and 10%of this alone will attribute to Internet of things [2]. Given these statistics, one can assume presenting, analysing and understanding large data needs appropriate visualization for comprehending and taking critical decisions based on this. It is also important to note that Real time data analytics require more apt visualization that appeal to the cognition of the person taking decisions.
Aims & Objectives
Understanding Real-time data analytics for visualization
Programming a framework/API to structure processing of information according to varying load.
Use of cloud computing to load-balance processing of the data.
Implement hierarchical visualization algorithm of data to improve cognitive ability of users to make critical judgments.
UI(User Interface) and UX (User Experience) development to enrich visual appeal while providing functionality.
Interfacing API/framework to suit different users.
Real time data analytics and its visualization are increasingly important in today's world of data. Current IDC reports state that by 2020, 50% of all business analytics software will incorporate prescriptive analytics built on cognitive computing functionality. It also states that big data-related professional services will have a compound annual growth rate of 23% by 2020. An article on EMC digital universe predicts digital data will increase up to 40% by the next decade, and 10%of this alone will attribute to Internet of things [2]. Given these statistics, one can assume presenting, analysing and understanding large data needs appropriate visualization for comprehending and taking critical decisions based on this. It is also important to note that Real time data analytics require more apt visualization that appeal to the cognition of the person taking decisions.
Aims & Objectives
Understanding Real-time data analytics for visualization
Programming a framework/API to structure processing of information according to varying load.
Use of cloud computing to load-balance processing of the data.
Implement hierarchical visualization algorithm of data to improve cognitive ability of users to make critical judgments.
UI(User Interface) and UX (User Experience) development to enrich visual appeal while providing functionality.
Interfacing API/framework to suit different users.
People |
ORCID iD |
| Nicolas S Holliman (Primary Supervisor) | |
| Manu Antony (Student) |
Publications
Nicolas S. Holliman
(2019)
Petascale Cloud Supercomputing for Terapixel Visualization of a Digital Twin
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/P510580/1 | 01/10/2016 | 30/09/2021 | |||
| 1965870 | Studentship | EP/P510580/1 | 01/10/2016 | 31/08/2020 | Manu Antony |
| Description | Designed and developed a visualization grammar for 3D data visualization. It was used to create visualizations for traffic saturation and found to be more effective than traditional UTC (Urban Traffic Control) systems to detect saturated traffic links. |
| Exploitation Route | The visualization grammar can be used for a wide variety of visualizations and data specialists. |
| Sectors | Digital/Communication/Information Technologies (including Software),Transport |