Business process adaptation on a tracked simulation model

Business processes need to adapt to changes in the operating conditions and to meet the service-level agreements (SLAs) with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system. An integral component to adaptation is the awareness over the behavior of self and environment (or having an estimation of the current situation). Aiming at estimation, this paper investigates the automatic building of a dynamic predictive model of the business process that is used for business process optimization. The model is a simulation model whose parameters are tuned at run time by tracking the system with a particle filter.

[1]  Eng Wah Lee,et al.  Business process management (BPM) standards: a survey , 2009, Bus. Process. Manag. J..

[2]  Mary Shaw,et al.  Engineering Self-Adaptive Systems through Feedback Loops , 2009, Software Engineering for Self-Adaptive Systems.

[3]  Paul J. Sánchez.  Fundamentals of simulation modeling , 2009, IEEE Engineering Management Review.

[4]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[5]  Andrei Romanenko,et al.  The unscented filter as an alternative to the EKF for nonlinear state estimation: a simulation case study , 2004, Comput. Chem. Eng..

[6]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[7]  Agata Filipowska,et al.  Semantic Business Process Management: A Lifecycle Based Requirements Analysis , 2007, SBPM.

[8]  Matjaz B. Juric,et al.  Business process execution language for web services , 2004 .

[9]  Emilio Paolucci,et al.  Redesigning Organisations Through Business Process Re-engineering and Object-orientation , 1997, ECIS.

[10]  Petar M. Djuric,et al.  Gaussian particle filtering , 2003, IEEE Trans. Signal Process..

[11]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[12]  Laury Verner.  BPM: The Promise and the Challenge , 2004, ACM Queue.

[13]  Mohamed Zairi,et al.  BPR implementation process: an analysis of key success and failure factors , 1999, Bus. Process. Manag. J..