David is a Reader in the Department of Computer Science, at Brunel University London. David is a keen multi-disciplinary researcher who has applied novel digital service solutions in a range of domains (including a range of health settings). His research interest lies in Service Design, Emotion AI, Cyber Security, Semantic technologies and Medical Technology (MedTech) – often driven by machine learning and simulation. David has collaborated on a number of funded research grants investigating the modelling of health evidence, augmenting naturalistic cultural settings with museum objects, trading personal data and simulating COVID-19. He has also worked with a number of NHS collaborators on the design of Apps that optimise or enhance patient pathways. David has supported the NHS advising on Health Data AI and Digital Hospital working groups. Before returning to university, David worked for several small and large software development companies and then in Investment Banking becoming technology director. More recently, David has commercialised his previous health evidence modelling research grant into HecoAnalytics Limited (a Brunel University London spin-out).
Abstract Automation of business transactions between trading partners is an important factor in t... more Abstract Automation of business transactions between trading partners is an important factor in today's global business. XML based e-Business standards are developed to provide a shared understanding on what information to share, when and how between trading partners. However these standards can only capture the syntax of the transactions and not the semantics. This paper presents an ontology for ebXML Business Process Specification Schema (ebBP), with the aim of empowering the capture and sharing semantics ...
Customer retention is a critical concern for most mobile network operators because of the increas... more Customer retention is a critical concern for most mobile network operators because of the increasing competition in the mobile services sector. This concern has driven companies to exploit data as an avenue to better understand customer needs. Data mining techniques such as clustering and classification have been adopted to understand customer retention in the mobile services industry. However, the effectiveness of these techniques is debatable due to the increasing complexity of the mobile market itself. This study proposes an application of Agent-Based Modeling and Simulation (ABMS) as a novel approach to understanding customer retention. A dataset provided by a mobile network operator is utilised to automate decision trees and agent based models. The most popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore customer churn scenarios. ABMS is used to understand the behavior of customers and dete...
Abstract Automation of business transactions between trading partners is an important factor in t... more Abstract Automation of business transactions between trading partners is an important factor in today's global business. XML based e-Business standards are developed to provide a shared understanding on what information to share, when and how between trading partners. However these standards can only capture the syntax of the transactions and not the semantics. This paper presents an ontology for ebXML Business Process Specification Schema (ebBP), with the aim of empowering the capture and sharing semantics ...
Customer retention is a critical concern for most mobile network operators because of the increas... more Customer retention is a critical concern for most mobile network operators because of the increasing competition in the mobile services sector. This concern has driven companies to exploit data as an avenue to better understand customer needs. Data mining techniques such as clustering and classification have been adopted to understand customer retention in the mobile services industry. However, the effectiveness of these techniques is debatable due to the increasing complexity of the mobile market itself. This study proposes an application of Agent-Based Modeling and Simulation (ABMS) as a novel approach to understanding customer retention. A dataset provided by a mobile network operator is utilised to automate decision trees and agent based models. The most popular churn modeling techniques were adopted in order to automate the development of models, from decision trees, and subsequently explore customer churn scenarios. ABMS is used to understand the behavior of customers and dete...
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