Knowledge modeling and optimization in pattern-oriented workflow generation

Automatic workflow generation is becoming an active research area for dealing with the dynamics of grid infrastructure. Artificial intelligence technology and explicit knowledge have been exploited in some research works for workflow construction or composition. With the increasing popularity of knowledge, its quality has growing impact on system performance. This paper proposed a synthesis method of pattern knowledge modeling and optimization for pattern based workflow generation planning. Experts define the primary modeling, and then the subsequent classifier training adjusts and improves the pattern knowledge settings. The experiments and application demonstrate that this approach can substantially reduce the modeling difficulties and effectively improve pattern knowledge quality.