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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.
Advanced technologies for data visualisation and data mining, developed in the Unit in collaboration with national and international teams, are widely applied for development of medical services. In particular, a system for canine lymphoma diagnosis and monitoring developed with [text removed for publication] has now been successfully tested using clinical data from several veterinary clinics. The risk maps produced by our technology provide early diagnosis of lymphoma several weeks before the clinical symptoms develop. [text removed for publication] has estimated the treatment test, named [text removed for publication], developed with the Unit to add [text removed for publication] to the value of their business. Institute Curie (Paris), applies this data mapping technique and the software that has been developed jointly with Leicester in clinical projects.
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.
Researchers in Cambridge have developed a data standard for storing and exchanging data between different programs in the field of macromolecular NMR spectroscopy. The standard has been used as the foundation for the development of an open source software suite for NMR data analysis, leading to improved research tools which have been widely adopted by both industrial and academic research groups, who benefit from faster drug development times and lower development costs. The CCPN data standard is an integral part of major European collaborative efforts for NMR software integration, and is being used by the major public databases for protein structures and NMR data, namely Protein Data Bank in Europe (PDBe) and BioMagResBank.
Data-to-text utilises Natural Language Generation (NLG) technology that allows computer systems to generate narrative summaries of complex data sets. These can be used by experts, professional and managers to better, and quickly, understand the information contained within large and complex data sets. The technology has been developed since 2000 by Prof Reiter and Dr Sripada at the University of Aberdeen, supported by several EPSRC grants. The Impact from the research has two dimensions.
As economic impact, a spinout company, Data2Text (www.data2text.com), was created in late 2009 to commercialise the research. As of May 2013, Data2Text had 14 employees. Much of Data2Text's work is collaborative with another UK company, Arria NLG (www.arria.com), which as of May 2013 had about 25 employees, most of whom were involved in collaborative projects with Data2Text.
As impact on practitioners and professional services, case studies have been developed in the oil & gas sector, in weather forecasting, and in healthcare, where NLG provides tools to rapidly develop narrative reports to facilitate planning and decision making, introducing benefits in terms of improved access to information and resultant cost and/or time savings. In addition the research led to the creation of simplenlg (http://simplenlg.googlecode.com/), an open-source software package which performs some basic natural language generation tasks. The simplenlg package is used by several companies, including Agfa, Nuance and Siemens as well as Data2Text and Arria NLG.
COnnecting REpositories (CORE) is a system for aggregating, harvesting and semantically enriching documents. As at July 2013, CORE contains 15m+ open access research papers from worldwide repositories and journals, on any topic and in more than 40 languages. In July 2013, CORE recorded 500k+ visits from 90k+ unique visitors. By processing both full-text and metadata, CORE serves four communities: researchers searching research materials; repository managers needing analytical information about their repositories; funders wanting to evaluate the impact of funded projects; and developers of new knowledge-mining technologies. The CORE semantic recommender has been integrated with digital libraries and repositories of cultural institutions, including the European Library and UNESCO. CORE has been selected to be the metadata aggregator of the UK's national open access services.
Bishop and Danicic contributed to the development of novel spend analysis software. Launched in 2011 as a commercial service by KTP industrial partners @UK PLC, SpendInsight has been used by over 380 organisations, including Basingstoke and North Hampshire NHS Foundation Trust, which, alone, cut procurement spend by £300,000 via savings identified using SpendInsight. An analysis produced by SpendInsight for the National Audit Office identified gross inefficiencies in NHS procurement, yielding potential annual overall savings of at least £500 million. The findings of this report were discussed in parliament and changes to NHS purchasing policy were recommended as a result.
This case study concerns the development and subsequent uptake of the Feature Selective Validation (FSV) method for data comparisons. The method has been adopted as the core of IEEE Standard 1597.1: a `first of its kind' standard on validation of computational electromagnetics and is seeing increasingly wide adoption in industry practice where comparison of data is needed, indicating the reach and significance of this work. The technique was developed by, and under the guidance of, Dr Alistair Duffy, who has remained the world-leading researcher in the field. The first paper on the subject was published in 1997 with key papers being published in 2006.
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.
Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.
Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.
As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.