Actor Petri net Model: toward Suitable and Flexible Level Representation of Scientific Workflows

Applications of scientific workflows are going to be more widespread and important to our living and lives. The intrinsic characteristics of scientific workflows are data- and computing-intensive, heterogeneous data representation and distributed execution environments. Scientists use different equipment to re- trieve source data by monitoring objects and then store or process on many differ- ent workstations. Actor Petri net Model (APnM) helps develop scientific work- flows effectively and efficiently within collaboration and cooperation work mode. Scientific workflow environments based on APnM can help scientists pay close attention to functional components development, and choose flexible mechanisms on error management, transaction and exception management, and priority pro- cessing. Scientific workflows represented based on APnM can be operated both on design and run time to support trial and error development. From the perspec- tive of software engineering, it is a suitable level of indirection to resolve the development, testing, and simulation complexity of scientific workflows.

[1]  Fabio Casati,et al.  Workflow Evolution , 1996, ER.

[2]  Frederica Darema,et al.  New Software Technologies for the Development and Runtime Support of Complex Applications , 1999, Int. J. High Perform. Comput. Appl..

[3]  James Hendler,et al.  Science and the Semantic Web , 2003, Science.

[4]  Jacek Sroka,et al.  DFL: A dataflow language based on Petri nets and nested relational calculus , 2008, Inf. Syst..

[5]  Ewa Deelman,et al.  Grids and Clouds: Making Workflow Applications Work in Heterogeneous Distributed Environments , 2010, Int. J. High Perform. Comput. Appl..

[6]  Min Luo,et al.  Supporting Dynamic Workflow Adaptation in a Dataflow-Constrained Workflow Net , 2009, 2009 International Conference on New Trends in Information and Service Science.

[7]  Shi Mei-lin A Meta-Model Supporting Dynamic Changing Workflow , 2002 .

[8]  Dennis Gannon,et al.  Scientific versus Business Workflows , 2007, Workflows for e-Science, Scientific Workflows for Grids.

[9]  Dennis Gannon,et al.  Workflows for e-Science, Scientific Workflows for Grids , 2014 .

[10]  Clarence A. Ellis,et al.  ML-DEWS: Modeling Language to Support Dynamic Evolution within Workflow Systems , 2000, Computer Supported Cooperative Work (CSCW).

[11]  Manfred Reichert,et al.  Adeptflex—Supporting Dynamic Changes of Workflows Without Losing Control , 1998, Journal of Intelligent Information Systems.

[12]  Qing Li,et al.  Actor Petri net model for scientific workflows: model, design and system , 2010, ICUIMC '10.