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Payment card fraud is a significant cost to business, as well as being a route to funding of organised crime, drug smuggling and terrorism. Detection of fraud requires a technique that is both transparent and adaptive. We have used the Department of Computing's expertise in machine learning and rule induction to develop a scalable method of automated fraud detection that meets the industry's needs. This technique is now being commercialised by AI Corporation, with a contract for its use having been placed by the world's largest retailer. Contracts with major banks are currently under negotiation.
The Computational Optimization Group (COG) in the Department of Computing produced new models, algorithms, and approximations for supporting confident decision-making under uncertainty — when computational alternatives are scarce or unavailable. The impact of this research is exemplified by the following:
Research in the HWCS Intelligent Systems Lab since 2006 has developed approaches to accelerate and improve large-scale optimization. This has led to new algorithms that enable multiple high-quality solutions for complex problems, either more quickly, with better solution quality than previously obtainable, or both. These algorithms, combined with uncertainty quantification techniques from related research, have been adopted by both British Petroleum Plc (BP) and Epistemy Ltd (an SME serving the oil/gas sector). Impact for BP includes improved business decision-making (relating to ~$330M in turnover),and impact for Epistemy includes sales of £230k.
Optimisation tools developed in the UoA have significantly advanced the ability to find the best designs for complex systems in cases where these were previously unobtainable. These optimisation tools have been implemented in several companies to shorten design times, reduce costs and reduce CO2 emissions. This has brought about new multi-million pound revenues, long-term contracts, increased employment and contribution to sustainability targets.
Short Term Conflict Alert systems are used by NATS to alert air traffic controllers to the risk of aircraft becoming dangerously close. This research has provided the means to enhance international air traffic safety by automatically optimising STCA systems so as to simultaneously maximise the number of alerts raised in response to truly dangerous situations, while, at the same time, minimising the number of false alerts. This has been achieved by developing multi-objective evolutionary algorithms to automatically locate the Pareto front describing the optimal trade-off between the numbers of true and false positives. The optimiser is described by NATS as "an outstanding improvement to our safety" [KTP-1395, final report].
Through close collaboration with scientists at the European Space Agency (ESA), research at the University of Southampton has developed new algorithms and an associated software tool that have contributed to more efficient spacecraft design. Now a standard component of the ESA's design technology, the tools have doubled the speed in which crucial design processes can be completed, resulting in increased efficiency over the REF period of 20 person-years — equivalent to €1 million in monetary terms — and maintaining the ESA's manufacturing competitiveness. The success of this work led to a €480,000 EU grant to adapt the tools for the avionics industry as part of efforts to meet ambitious environmental targets under the EU Clean Sky Initiative.
Efficient city-wide water distribution systems (WDS) are vital to the health and financial wellbeing of the cities' inhabitants. The impact of research on evolutionary algorithms and heuristics at Exeter has been to provide efficient water distribution system designs on a city-wide basis for a large city, the City of Ottawa. As part of a programme of building and upgrading infrastructure costing in excess of $225M CAD, the City has implemented large-scale capital projects based on designs produced by genetic algorithm technology, made possible by utilising the heuristics and modular optimisation developed by Keedwell and colleagues at the University of Exeter.
This case study reports our work on the development, application and dissemination of innovative cloud-based technologies to industrial problem domains. First, decentralised scheduling is implemented within federated Clouds, to facilitate the new drug discovery process for a global pharmaceutical company. Second, multi-objective approaches to the management and optimisation of video processing and analysis workflows in distributed environments is described in the context of an SME organisation that is developing new products, services and markets. Both of these examples have attracted, and continue to attract, commercial funding, and demonstrate the efficacy of knowledge transfer into industry from University of Derby (UoD) research.
Visual analytics is a powerful method for understanding large and complex datasets that makes information accessible to non-statistically trained users. The Non-linearity and Complexity Research Group (NCRG) developed several fundamental algorithms and brought them to users by developing interactive software tools (e.g. Netlab pattern analysis toolbox in 2002 (more than 40,000 downloads), Data Visualisation and Modelling System (DVMS) in 2012).
Industrial products. These software tools are used by industrial partners (Pfizer, Dstl) in their business activities. The algorithms have been integrated into a commercial tool (p:IGI) used in geochemical analysis for oil and gas exploration with a 60% share of the worldwide market.
Improving business performance. As an enabling technology, visual analytics has played an important role in the data analysis that has led to the development of new products, such as the Body Volume Index, and the enhancement of existing products (Wheelright: automated vehicle tyre pressure measurement).
Impact on practitioners. The software is used to educate and train skilled people internationally in more than 6 different institutions and is also used by finance professionals.
Pricing optimisation and revenue management systems represent fundamental progress from the art of pricing to the science of pricing. Our research led the scientific approach in demand modelling and pricing optimization, and produced the first computerised Intelligent Pricing Decision Support Systems (IPDSS) for retail and petroleum, which have led to economic impact and changes in pricing practice. Our research led to spin-off companies that employ over 150 people, with a turnover of £19.2m in 2012, which are the leading providers of IPDSS, used by more than 400 retailers across 80 countries to improve their performance in competitive markets.