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Decision Systems & e-Service Intelligence
Organisational decision making, in today’s complex and dynamic environment, is growing ever more difficult. Decision-makers need advanced knowledge of consumer behaviour and accurate analysis of big data to predict trends, respond to consumer demand, negotiate effective contracts and develop products and services that will thrive in increasingly competitive conditions.

The DeSI Lab is working to develop theories, methods and software systems to help organisations make better, more informed decisions and give them an edge as they chart their course through the sea of Big Data.

The DeSI Lab is highly successful in exploring theoretical and methodological questions in the field of decision systems and also actively commits to solving the real-world problems of government and industry.  Their work focuses on model driven and data-driven decision support systems, prediction and early warning systems, situation awareness and risk management, recommender systems, fuzzy transfer learning, concept drift, and multi-criteria, multi-level decision-making, and typically results in powerful software tools organisation’s can use to support decision making.

Their extensive collaboration networks, including distinguished researchers in Canada, Belgium, Germany, Spain, France, Japan, USA, China and the UK play a vital role in creating the links between theoretical breakthroughs and business-ready software tools.

The DeSI Lab also plays an important role in integrating research with teaching. Over the last six years, the Lab has organised 12 successful workshops on a variety of topics, offering staff and students the opportunity to meet and work with high-profile visitors and access up-to-the-minute advances in their field. And, over the last 10 years, staff have received 10 internal and national teaching and learning grants and been involved in the organisation and chairing of two international conferences – FLINS (Fuzzy Logic & Intelligent Technologies in Nuclear Science) and ISKE (Intelligent Systems and Knowledge Engineering).

Research Interests

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Decision support systems
 
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Fuzzy logic and measure
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Multi-criteria decision making
 
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Fuzzy optimisation and fuzzy decision making
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Multi-level decision making
 
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Uncertain information processing
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Cognitive decision models and analysis
 
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Situation awareness
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Early warning systems
 
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Web intelligence and ontology
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e-Service and e-Business intelligence systems
 
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e-Government service personalization and integration
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Recommendation systems
 
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Case-based reasoning and prediction
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Emergency management and risk analysis
 
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Genetic algorithms
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Online negotiation and online auction
 
 
 

 

Application Domains

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Organisational decision-making
 
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Social crisis prediction, early warning and anti-terrorism
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Material, product and service evaluation
 
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Knowledge management and adaption
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Business strategy and resource planning
 
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e-Government and m-Government
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Logistics and customer relationship management 
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Risk management in various areas such as textile and fashion design, power market, transportation, nuclear engineering, education, tourism and telecomms