Journal of Global Optimization

, Volume 51, Issue 2, pp 285–300 | Cite as

Maturity, distance and density (MD2) metrics for optimizing trust prediction for business intelligence

  • Muhammad Raza
  • Omar Khadeer Hussain
  • Farookh Khadeer HussainEmail author
  • Elizabeth Chang


The modelling and management of trust between interacting parties are crucial parts of the overall business intelligence strategy for any organization. Predicting trust values is a key element of modelling and managing trust. It is of critical importance when the interaction is to be conducted at a future point in time. In the existing body of work, there are a few approaches for predicting trust. However, none of these approaches proposes a framework or methodology by which the predicted trust value can be considered in light of its accuracy or confidence level. This is a key element in order to ensure optimized trust prediction. In this paper, we propose a methodology to address this critical issue. The methodology comprises a suite of metrics—maturity, distance and density (MD2) which are capable of capturing various aspects of the confidence level in the predicted trust value. The proposed methodology is exemplified with the help of case studies.


Trust Trust prediction Optimization Prediction Business intelligence Optimized business intelligence 


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Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Muhammad Raza
    • 1
  • Omar Khadeer Hussain
    • 1
  • Farookh Khadeer Hussain
    • 1
    Email author
  • Elizabeth Chang
    • 1
  1. 1.Digital Ecosystems and Business Intelligence InstituteCurtin University of TechnologyBentleyAustralia

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