中国科学院自动化研究所机构知识库

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.

Considering the knowledge-intensity and error-prone of developing scientific workflows from scratch, reusing and repurposing current workflows are the effective and efficient solution to support scientists for conducting novel experiments, and this strategy is deemed as important to achieve the objective of smart campus. An experiment may be relevant with one or multiple scientific workflows. This observation drives us to propose a technique that can discover and recommend cross-workflow fragments with respect to the requirement of novel experiments. Specifically, the functionally similar activities are clustered through adopting a modularity-based community discovery clustering technique, and they are represented as abstract activities. An abstract activity network model is constructed accordingly to reflect the invocation relations among abstract activities. Structural and semantic similar workflow fragments are discovered from the abstract activity network through the sub-graph matching algorithm. These fragments are instantiated through replacing abstract activities by appropriate activities in certain activity clusters. These instantiated workflow fragments are ranked and recommended for their reuse and repurposing purpose. Experimental evaluation results demonstrate that our technique is accurate and efficient on discovering and recommending appropriate cross-workflow fragments.