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

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.

[1]  Vijay Varadharajan,et al.  Fuzzy Regression Based Trust Prediction in Service-Oriented Applications , 2009, ATC.

[2]  E. Chang,et al.  Trust Issues in Service Oriented Environment , 2006, 2006 IEEE International Conference on Service Operations and Logistics, and Informatics.

[3]  Jonas Mockus,et al.  On simulation of optimal strategies and Nash equilibrium in the financial market context , 2010, J. Glob. Optim..

[4]  Schahram Dustdar,et al.  Start Trusting Strangers? Bootstrapping and Prediction of Trust , 2009, WISE.

[5]  Rimvydas Simutis,et al.  Development and Evaluation of Decision-Making Model for Stock Markets , 2006, J. Glob. Optim..

[6]  Geok See Ng,et al.  Neural network model for time series prediction by reinforcement learning , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[7]  Chenyi Hu,et al.  An Application of Interval Methods to Stock Market Forecasting , 2007, Reliab. Comput..

[8]  Yutaka Matsuo,et al.  Community gravity: measuring bidirectional effects by trust and rating on online social networks , 2009, WWW '09.

[9]  John L. Klepeis,et al.  Ab initio Tertiary Structure Prediction of Proteins , 2003, J. Glob. Optim..

[10]  Junyi Shen,et al.  A new Markov model for Web access prediction , 2002, Comput. Sci. Eng..

[11]  P. Pardalos,et al.  Pareto optimality, game theory and equilibria , 2008 .

[12]  Prem Kumar Kalra,et al.  Time Series Prediction with Multilayer Perceptron (MLP): A New Generalized Error Based Approach , 2008, ICONIP.

[13]  Ee-Peng Lim,et al.  Trust relationship prediction using online product review data , 2009, CIKM-CNIKM.

[14]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[15]  C. Siriopoulos,et al.  Combinatorial time series forecasting based on clustering algorithms and neural networks , 2004, Neural Computing & Applications.

[16]  Enriqueta Vercher,et al.  SIOPRED: a prediction and optimisation integrated system for demand , 2008 .

[17]  Liping Wang,et al.  Evolutionary Prediction of Online Keywords Bidding , 2008, EC-Web.

[18]  Adam Wierzbicki,et al.  Enriching Trust Prediction Model in Social Network with User Rating Similarity , 2009, 2009 International Conference on Computational Aspects of Social Networks.

[19]  E. Chang,et al.  Trust and Reputation for Service-Oriented Environments: Technologies For Building Business Intelligence And Consumer Confidence , 2006 .

[20]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[21]  Charles C. Holt,et al.  Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[22]  Xin Wang,et al.  Time-line hidden Markov experts for time series prediction , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[23]  S. Soliman,et al.  Fuzzy short-term electric load forecasting using Kalman filter , 2006 .

[24]  Markos Papageorgiou,et al.  Long‐term sales forecasting using holt–winters and neural network methods , 2005 .

[25]  Alice Bob,et al.  The PGP Trust Model , 2005 .

[26]  Christophe Croux,et al.  Computational aspects of robust Holt-Winters smoothing based on M-estimation , 2008 .

[27]  James Nga-Kwok Liu,et al.  iJADE WeatherMAN: a weather forecasting system using intelligent multiagent-based fuzzy neuro network , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[28]  Zoran Obradovic,et al.  Rapid design of neural networks for time series prediction , 1996 .

[29]  T.M. McGinnity,et al.  A time-series prediction approach for feature extraction in a brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[30]  Jaideep Srivastava,et al.  Predicting trusts among users of online communities: an epinions case study , 2008, EC '08.

[31]  Licia Capra,et al.  Autonomic trust prediction for pervasive systems , 2006, 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06).

[32]  P. McSharry,et al.  Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.

[33]  Panos M. Pardalos,et al.  Handbook of applied optimization , 2002 .

[34]  E. Chang,et al.  Markov model for modelling and managing dynamic trust , 2005, INDIN '05. 2005 3rd IEEE International Conference on Industrial Informatics, 2005..

[35]  Jesús García,et al.  Subjective Trust Inferred by Kalman Filtering vs. a Fuzzy Reputation , 2004, ER.

[36]  Elizabeth Chang,et al.  A robust methodology for prediction of trust and reputation values , 2008, SWS '08.

[37]  Stephen Hailes,et al.  Supporting trust in virtual communities , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[38]  Jacek Mandziuk,et al.  Neuro-evolutionary approach to stock market prediction , 2007, 2007 International Joint Conference on Neural Networks.

[39]  C. Holt.  Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .