Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chang E., Dillon T., Hussain F.: Trust and Reputation for Service-Oriented Environments: Technologies for Building Business Intelligence and Consumer Confidence. Wiley, West Sussex (2006)

    Book  Google Scholar 

  2. Abdul-Rahman, A.: The PGP trust model. J. Electron. Commerce (1997)

  3. Abdul-Rahman, A., Hailes, S.: Supporting trust in virtual communities. In: HICSS 2000, pp. 1769–1777. (2000)

  4. Hussain, F.K., Chang, E., Hussain, O.: A robust methodology for prediction of trust and reputation values. In: ACM Workshop on Secure Web Services (SWS 2008) with 15th ACM Conference on Computer and Communications Security, pp. 97–108. ACM, Virginia (2008)

  5. Hussain, F., Chang, E., Dillon, T.: Markov model for managing dynamic trust. In: INDIN 2005: 3rd International Conference on Industrial Informatics, Frontier Technologies for the Future of Industry and Business, pp. 725–733. IEEE, Perth (2005)

  6. Hassan, M.R., Nath, B.: StockMarket forecasting using hidden Markov model: a new approach. In: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, pp. 192–194. Wroclaw, Poland (2005)

  7. Kalman R.E.: A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82(Series D), 35–45 (1960)

    Google Scholar 

  8. Brockwell P.J., Davis R.A.: Introduction to Time Series and Forecasting. Springer, Berlin (2002)

    Book  Google Scholar 

  9. Capra, L., Musolesi, M.: Autonomic trust prediction for pervasive systems. In: Proceedings of the 20th International Conference on Advanced Information Networking and Applications, Vienna, Austria, pp. 481–488 (2006)

  10. Holt C.C.: Forecasting seasonals and trends by exponentially weighted moving averages. Int. J. Forecast. 20, 5–10 (2004)

    Article  Google Scholar 

  11. Kotsialos A., Papageorgiou M., poulimenos A.: Long-term sales forecasting using Holt–Winters and neural network methods. J. Forecast. 24, 353–368 (2005)

    Article  Google Scholar 

  12. Croux C., Gelper S., Fried R.: Computational aspects of robust Holt-Winters smoothing based on M -estimation. Appl. Math. 53, 163–176 (2008)

    Article  Google Scholar 

  13. Matsuo, Y., Yamamoto, H.: Community gravity: measuring bidirectional effects by trust and rating on online social networks. In: Proceedings of the 18th International Conference on World Wide Web. Madrid, Spain, pp. 751–760 (2009)

  14. Ma, N.: Trust relationship prediction using online product review data. In: CNIKM 2009. ACM. Hong Kong, China, pp. 47–54 (2009)

  15. Carbo, J., Garcia, J., Molina, J.M.: Subjective trust inferred by Kalman filtering vs. a fuzzy reputation. In: Proceedings of 23rd International Conference on Conceptual Modeling, Shanghai, China, vol 3289, pp. 496–505 (2004)

  16. Hussain, F.K.: A methodology for trust management in service oriented environment. PhD Thesis, Curtin University of Technology, Australia (2006)

  17. Wang, X., Whirgham, P., Deng, D.: Time-line hidden Markov experts for time series prediction. In: Proceedings of Neural Networks and Signal Processing. IEEE Computer Society, pp. 786–789. (2003)

  18. Shiblee, M., Kalra, P.K., Chandra, B.: Time Series Prediction with Multilayer Perceptron (MLP): A New Generalized Error Based Approach. Lecture Notes in Computer Science, 5507. Springer, Berlin/Heidelberg (2009)

  19. Liu, F., Quek, C., Ng. G.S.: Neural network model for time series prediction by reinforcement learning. In: IEEE International Joint Conference on Neural Networks. IEEE Computer Society. Montreal, Canada (2005)

  20. Coyle D., Prasad G., McGinnity T.M.: A time-series prediction approach for feature extraction in a brain–computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 13(4), 461–467 (2005)

    Article  Google Scholar 

  21. Sfetsos A., Siriopoulos C.: Combinatorial time series forecasting based on clustering algorithms and neural networks. Neural Comput. Appl. 13(1), 56–64 (2004)

    Article  Google Scholar 

  22. Drossu R., Obradovic Z.: Rapid design of neural networks for time series prediction. IEEE Comput. Sci. Eng. 3(2), 78–89 (1996)

    Article  Google Scholar 

  23. Dongshan X., Junyi S.: A new Markov model for Web access prediction. Comput. Sci. Eng. 4(6), 34–39 (2002)

    Article  Google Scholar 

  24. Bermúdez J.D., Segura J.V., Vercher E.: SIOPRED: a prediction and optimisation integrated system for demand. TOP 16(2), 258–271 (2008)

    Article  Google Scholar 

  25. Hou, L., Wang, L., Yang, J.: Evolutionary prediction of online keywords bidding. In: E-Commerce and Web Technologies, pp. 124–133. Springer, Berlin/Heidelberg (2008)

  26. Borzymek, P., Sydow, M., Wierzbicki, A.: Enriching trust prediction model in social network with user rating similarity. In: CASON 2009, pp. 40–47. IEEE Computer Society, Fontainebleau, France (2009)

  27. Liu, H.: Predicting trusts among users of online communities—an Epinions case study. In: ACM Conference on Electronic Commerce, pp. 310–311. ACM, Chicago, IL, USA (2008)

  28. Skopik, F., Schall, D., Dustdar, S.: Start trusting strangers? Bootstrapping and prediction of trust. In: WISE 2009, pp. 275–289 Springer, Berlin (2009)

  29. Li, L., Wang, Y., Varadharajan, V.: Fuzzy regression based trust prediction in service-oriented applications. In: Autonomic and Trusted Computing 2009, pp. 221–235. Springer, Berlin (2009)

  30. Hassan, M.R., Nath, B.: StockMarket forecasting using hidden Markov model: a new approach. In: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, pp. 192–196 (2005)

  31. Mańdziuk, J., Jaruszewicz, M.: Neuro-evolutionary approach to stock market prediction. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2007), Orlanodo, FL, USA, pp. 2515–2520. IEEE Press, Los Alamitos (2007)

  32. Hu C., He L.T.: An application of interval methods to stock market forecasting. Reliable Comput. 13(5), 423–434 (2007)

    Article  Google Scholar 

  33. Al-Hamadi H.M., Soliman S.A.: Fuzzy short-term electric load forecasting using Kalman filter[J]. IEEE Proc.Gener. Trans. Distrib. 153(2), 217–227 (2006)

    Article  Google Scholar 

  34. Taylor J.W., McSharry P.E.: Short-term load forecasting methods: an evaluation based on European data. IEEE Trans. Power Syst. 22, 2213–2219 (2007)

    Article  Google Scholar 

  35. Lee R., Liu J.: iJADE WeatherMAN : a weather forecasting system using intelligent multiagent-based fuzzy neuro network. IEEE Trans. Syst. Man Cybern. C 34, 369–377 (2004)

    Article  Google Scholar 

  36. Mockus, J.: On simulation of optimal strategies and Nash equilibrium in the financial market context. J. Global Optim. ISSN 1573-2916 (2009)

  37. Nenortaite J., Simutis R.: Development and evaluation of decision-making model for stock markets. J. Global Optim. 36(1), 1–19 (2006)

    Article  Google Scholar 

  38. Klepeis J.L., Floudas C.A.: Ab initio tertiary structure prediction of proteins. J. Global Optim. 25, 113–140 (2003)

    Article  Google Scholar 

  39. Chinchuluun, A., Pardalos, P., Migdalas, A., Pitsoulis, L. (eds): Pareto Optimality, Game Theory and Equilibria. Springer, Berlin (2008)

    Google Scholar 

  40. Zopounidis, K., Pardalos, P.M., Baourakis, G. (eds): Fuzzy systems in management, economy and marketing. World Scientific, Singapore (2001)

    Google Scholar 

  41. Pardalos, P.M., Resende, M. (eds): Handbook of Applied Optimization. Oxford University Press, Oxford (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, Bentley, WA, 6102, Australia

    Muhammad Raza, Omar Khadeer Hussain, Farookh Khadeer Hussain & Elizabeth Chang

Authors
  1. Muhammad Raza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omar Khadeer Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farookh Khadeer Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elizabeth Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Farookh Khadeer Hussain.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Raza, M., Hussain, O.K., Hussain, F.K. et al. Maturity, distance and density (MD2) metrics for optimizing trust prediction for business intelligence. J Glob Optim 51, 285–300 (2011). https://doi.org/10.1007/s10898-010-9598-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-010-9598-5

Keywords

Search

Navigation