Multi-level Autonomic Business Process Management

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38484-4_14Nowadays, business processes are becoming increasingly complex and heterogeneous. Autonomic Computing principles can reduce this complexity by autonomously managing the software systems and the running processes, their states and evolution. Business Processes that are able to be self-managed are referred to as Autonomic Business Processes (ABP). However, a key challenge is to keep the models of such ABP understandable and expressive in increasingly complex scenarios. This paper discusses the design aspects of an autonomic business process management system able to self-manage processes based on operational adaptation. The goal is to minimize human intervention during the process definition and execution phases. This novel approach, named MABUP, provides four well-defined levels of abstraction to express business and operational knowledge and to guide the management activity; namely, Organizational Level, Technological Level, Operational Level and Service Level. A real example is used to illustrate our proposal.Research supported by CAPES, CNPQ and Spanish Ministry of Science and Innovation.Oliveira, K.; Castro, J.; España Cubillo, S.; Pastor López, O. (2013). Multi-level Autonomic Business Process Management. En Enterprise, Business-Process and Information Systems Modeling. Springer. 184-198. doi:10.1007/978-3-642-38484-4_14S184198España, S., González, A., Pastor, Ó.: Communication Analysis: A Requirements Engineering Method for Information Systems. In: van Eck, P., Gordijn, J., Wieringa, R. (eds.) CAiSE 2009. LNCS, vol. 5565, pp. 530–545. Springer, Heidelberg (2009)Ganek, A.G., Corbi, T.A.: The dawning of the autonomic computing era. IBM Systems Journal 42(1), 5–18 (2003)Gonzalez, A., et al.: Unity criteria for Business Process Modelling. In: Third International Conference on Research Challenges in Information Science, RCIS 2009, pp. 155–164 (2009)Greenwood, D., Rimassa, G.: Autonomic Goal-Oriented Business Process Management. Management, 43 (2007)Haupt, T., et al.: Autonomic execution of computational workflows. In: 2011 Federated Conference on Computer Science and Information Systems, FedCSIS, pp. 965–972 (2011)Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE (2003)Lee, K., et al.: Workflow adaptation as an autonomic computing problem. In: Proceedings of the 2nd Workshop on Workflows in Support of Large-Scale Science, New York, NY, USA, pp. 29–34 (2007)Mosincat, A., Binder, W.: Transparent Runtime Adaptability for BPEL Processes. In: Bouguettaya, A., Krueger, I., Margaria, T. (eds.) ICSOC 2008. LNCS, vol. 5364, pp. 241–255. Springer, Heidelberg (2008)Oliveira, K., et al.: Towards Autonomic Business Process Models. In: International Conference on Software Engineering and Knowledge, SEKE 2012, San Francisco, California, USA (2012)Rahman, M., et al.: A taxonomy and survey on autonomic management of applications in grid computing environments. Concurr. Comput.: Pract. Exper. 23(16), 1990–2019 (2011)Reijers, H.A., Mendling, J.: Modularity in process models: Review and effects. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 20–35. Springer, Heidelberg (2008)Rodrigues Nt., J.A., Monteiro Jr., P.C.L., de O. Sampaio, J., de Souza, J.M., Zimbrão, G.: Autonomic Business Processes Scalable Architecture. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 78–83. Springer, Heidelberg (2008)Strohmaier, M., Yu, E.: Towards autonomic workflow management systems. ACM Press (2006)Terres, L.D., et al.: Selection of Business Process for Autonomic Automation. In: 2010 14th IEEE International Enterprise Distributed Object Computing Conference, pp. 237–246 (October 2010)Tretola, G., Zimeo, E.: Autonomic internet-scale workflows. In: Proceedings of the 3rd International Workshop on Monitoring, Adaptation and Beyond, New York, NY, USA, pp. 48–56 (2010)Vedam, H., Venkatasubramanian, V.: A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis. In: Proceedings of the 1997 American Control Conference, vol. 1, pp. 309–313 (June 1997)Wang, Y., Mylopoulos, J.: Self-Repair through Reconfiguration: A Requirements Engineering Approach. In: 2009 IEEE/ACM International Conference on Automated Software Engineering, pp. 257–268 (November 2009)Yu, T., Lin, K.: Adaptive algorithms for finding replacement services in autonomic distributed business processes. In: Proceedings Autonomous Decentralized Systems, ISADS 2005, pp. 427–434 (2005

Utilizing the blackboard paradigm to implement a workflow engine

Workflow management has evolved into a mature field with numerous workflow management systems with scores of features. These systems are designed to automate business processes of organisations. However, many of these workflow engines struggle to support complex workflows. There has been relatively little research into building a workflow engine utilizing the blackboard paradigm. The blackboard paradigm can be characterized as specialists interacting with and updating a centralized data structure, namely the blackboard, with partial and complete solutions. The opportunistic control innate to the blackboard paradigm can be leveraged to support the execution of complex workflows. Furthermore, the blackboard architecture can be seen to accommodate comprehensive workflow functionality. This research aims to verify whether or not the blackboard paradigm can be used to build a workflow engine. To validate this research, a prototype was designed and developed following stringent guidelines in order to remain true to the blackboard paradigm. Four main perspectives of workflow management namely the functional, behavioural, informational and operational aspects with their quality indicators and requirements were used to evaluate the prototype. This evaluation approach was chosen since it is universally applicable to any workflow engine and thereby provides a common platform on which the prototype can be judged and compared against other workflow engines. The two most important quality indicators are the level of support a workflow engine can provide for 20 main workflow patterns and 40 main data patterns. Test cases based on these patterns were developed and executed within the prototype to determine the level of support. It was found that the prototype supports 85% of all the workflow patterns and 72.5% of all the data patterns. This reveals some functional limitations in the prototype and improvement suggestions are given that can boost these scores to 95% and 90% for workflow and data patterns respectively. The nature of the blackboard paradigm only prevents support of only 5% and 10% of the workflow and data patterns respectively. The prototype is shown to substantially outperform most other workflow engines in the level of patterns support. Besides support for these patterns, other less important quality indicators provided by the main aspects of workflow management are also found to be present in the prototype. Given the above evidence, it is possible to conclude that a workflow engine can be successfully built utilizing the blackboard paradigm