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A  joint US/UK research programme has been set up to unlock the potential of big data in front line situations, where people and computer systems need to collaborate in a coalition of multiple agencies.

The programme is called the International Technology Alliance in Distributed Analytics and Information Science (DAIS ITA).

Image of a city with skyscrapers linked together by lines of data
Recent advances in areas like artificial intelligence and machine learning have greatly improved the capabilities of technology to assist coalitions working together in rapidly-changing situations, such as major disasters, to make people safer.

Cardiff University’s Crime and Security Research Institute is a key part of DAIS ITA, which is led by IBM, along with leading universities in the UK and the US and major international businesses, such as Airbus Group and BAE Systems. The alliance has been established by the US Army Research Laboratory (ARL) and the UK Defence Science and Technology Laboratory (Dstl) with up to $80m (£63m) in funding to address this challenge over the next 10 years.

The DAIS ITA research at Cardiff is led by Professor Alun Preece, Co-Director of the Crime and Security Research Institute, together with Professor Roger Whitaker and Dr Ian Taylor from the School of Computer Science and Informatics.

In addition to Cardiff University, academic and industry partners include:

  • Airbus Group
  • BAE Systems
  • IBM
  • Imperial College London
  • Pennsylvania State University
  • Purdue University
  • Raytheon/BBN Technologies
  • Stanford University
  • University College London
  • University of California at Los Angeles
  • University of Massachusetts at Amherst
  • University of Southampton
  • Yale University
Map of DAIS ITA members in the US and UK
DAIS ITA members are situated across the US and UK in an international collaboration.

DAIS ITA continues a successful model for academic-industrygovernment collaboration established in the first ITA programme – the Network and Information Sciences (NIS) ITA – which ran between 2006 and 2016. The key driver is collaboration among experts from multiple disciplines to address challenge problems co-defined with the government stakeholders from the UK Ministry of Defence and US Department of Defense. The primary purpose of the alliance is to make scientific advancements that are both innovative and have the potential to be disruptive, i.e., to introduce radical and evidence-based novel approaches into the theory and practice of coalition operations. The ITA model facilitates the exploitation of these scientific advances through technology transition into the military and civil sectors, as well as enriching the UK and US science and technology (S&T) capabilities.

When we think of big data we usually think of collections of computer servers in huge data centres that we access via the cloud. In this project we’re trying to turn the data centre inside out by collecting and processing data near the edges of the network, creating dynamic, virtual data centres surrounding teams of front line responders.

Professor Alun Preece, UK Academic Technical Area Lead
Infographic demonstrating the DAIS ITA collaboration model

Example projects

Instinctive analytics

In a modern coalition network, data is collected from sensing systems such as drones, satellites or robotic ground vehicles, and then processed by various computational techniques including signal processing and machine learning. The processed data is then delivered to human decision-makers in forms they can understand, such as natural language or pictorial visualisations. The DAIS ITA programme takes a unique perspective on the coalition network, viewing it as a “distributed brain” and re-imagining the traditional data analytic pipeline – from sensors to analytics to human – in terms of a neural network architecture used to simulate human cognition. Early experimental results show the system being highly resilient and adaptable to changes in the network environment that often happen in rapidly-evolving emergency situations.

Collaborators include Cardiff, Pennsylvania State University, Stanford University, University of Southampton, Yale, Airbus, IBM

Anticipatory situational understanding

Advances in sensing technologies and data analytics – especially machine learning – mean that computer systems are increasingly able to help people maintain awareness of an evolving situation and predict possible future events. However, current machine learning systems have two major limitations: (1) they need a huge amount of data to learn from, and many important kinds of event are relatively rare, meaning that computers need to be “told” rather than “taught”; and (2) they are typically unable to explain their predictions in ways that are meaningful to humans. This project is the first to study these problems – tellability and explainability – in the context of coalition operations, leading to the proposal of novel ways of linking humanmachine communication, reasoning, and learning.

Collaborators include Cardiff, University College London, University of California at Los Angeles, Airbus, BAE Systems, IBM

Evolution of human systems

Successful multi-agency operations often involve understanding complex situations involving multiple groups with varying aims and capabilities. DAIS ITA is undertaking basic research into mutability (changeabilty) of group-based human systems, and it can be modelled. Early work has identified five critical features of social groups that govern mutability, relating to social norms, individual standing, status rivalry, ingroup bias and cooperation. All these features lend themselves to computer modelling. The research also considers the important role of online social media in relation to understanding the mutability of groups. This can play an active role in supporting collective behaviour. Analysis of social media in the context of the five dimensions of group mutability provides a fresh basis to interpret the forces affecting groups.

Collaborators include Cardiff, Pennsylvania State University, University of Southampton, Yale, Airbus, IBM