Temporal constraints are often set when complex e-science processes are modelled as scientific workflow specifications. However, many existing processes such as climate modelling often have only a few coarse-grained temporal constraints globally. This is not sufficient to control overall temporal correctness as we can not find temporal violations locally in time for handling. Local handling affects fewer workflow activities, hence more cost effective than global handling with coarse-grained temporal constraints. Therefore, in this paper, we systematically investigate how to localise a group of fine-grained temporal constraints so that temporal violations can be indentified locally for better handling cost effectiveness. The corresponding algorithms are developed. The quantitative evaluation demonstrates that with local fine-grained temporal constraints, we can improve handling cost effectiveness significantly than only with coarse-grained ones.
