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Details of Grant 

EPSRC Reference: EP/P025501/1
Title: nlvis: Natural Language Interaction for Visual Data Analysis
Principal Investigator: Turkay, Dr C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Redsift Limited Incorporated
Department: Computing
Organisation: City, University of London
Scheme: First Grant - Revised 2009
Starts: 01 December 2017 Ends: 31 May 2019 Value (£): 100,850
EPSRC Research Topic Classifications:
Artificial Intelligence Computational Linguistics
Computer Graphics & Visual.
EPSRC Industrial Sector Classifications:
Creative Industries
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 Mar 2017 EPSRC ICT Prioritisation Panel March 2017 Announced
Summary on Grant Application Form
The unprecedented increase in the amount, variety and value of data has been significantly transforming the way that scientific research is carried out and businesses operate. As data sources become increasingly diverse and complex, analysis approaches where the human and the computer operate in collaboration have proven to be an effective approach to derive actionable observations. This is achieved through an iterative human-computer dialogue where the knowledge and the creativity of the human meets the power of computation. In such human-in-the-loop data analysis approaches, interactive visualisation methods are core facilitators of this dialogue. However, these methods still rely on conventional, not often intuitive interaction mechanisms that can introduce unnecessary complexities into the process. There is an urgent need to rethink the ways how analysts interact with visualisations in data-intensive analysis situations. The recent advances in natural language based interaction methodologies offer promising avenues to address that.

This project aims to develop a fundamental understanding of how analysts can use natural language elements to perform visualisation empowered data analysis and use that understanding to develop a framework where natural language and visualisation based interactions operate in harmony. The project then aims to demonstrate how such a multi-modal interaction scheme can radically transform the analysts' experience with the goal of achieving significant improvements in the value and the volume of actionable observations generated.

Within the project, we will initially identify and develop a taxonomy of natural language interaction elements for describing visualisations and for carrying out a visual data analysis process. Here, we will inform our investigation with findings from data collected through crowd-based survey methodologies. We will then design a conceptual framework that facilitates an iterative data analysis process through interactions with both natural language and visualisation elements. We will make use of the data analysis and visualisation related language taxonomy from the earlier stage to define the scope and the capabilities of the interaction elements.

The project will then move on to realising its vision through a prototype where the conceptual framework will operate through the help of an established conversational interface mechanism. The prototype will involve a combination of natural language and visual interaction capabilities and will also incorporate underlying computational capacities. We will then evaluate our approaches through a series of carefully designed use-cases that encompass common visual analysis scenarios. Our success criteria will be to achieve enhanced engagement and improved productivity during the visual analysis of complex data-intensive problems.

Potential beneficiaries of the outputs of this project ranges widely from academic researchers, professional data analysts, data analysis industry, and the general public. For visualisation and visual analytics researchers, findings will benefit researchers who are working on understanding user-intent and mechanisms of sense-making in interactive visual analysis processes. For businesses that offer visualisation-empowered solutions to their customers (according to some reports, the visualisation market size is expected to reach a $2.8 Billion by 2020), the framework developed will provide the basis for new forms of products that are easier to learn and engage with. For professional data analysts, the novel interaction capability will offer a more fluid and natural experience, improving their efficiency and positively impacting the quality of observations. For the general public, natural interaction mechanisms will provide an enhanced experience when using data-intensive products that are becoming to be widely adopted.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
Sectors submitted by the Researcher
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Organisation Website: http://www.city.ac.uk