Diagnosing correctness of semantic workflow models
To model operational business processes in an accurate way, workflow models need to reference both the control flow and dataflow perspectives. Checking the correctness of such workflow models and giving precise feedback in case of errors is challenging due to the interplay between these different pe...
| Authors: | , , , |
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| Format: | article |
| Status: | Versión enviada para evaluación y publicación |
| Publication Date: | 2013 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/130104 |
| Online Access: | https://hdl.handle.net/11441/130104 https://doi.org/10.1016/j.datak.2013.04.008 |
| Access Level: | Open access |
| Keyword: | Workflow Business processes management Diagnosis Constraint programming Integer programming |
| Summary: | To model operational business processes in an accurate way, workflow models need to reference both the control flow and dataflow perspectives. Checking the correctness of such workflow models and giving precise feedback in case of errors is challenging due to the interplay between these different perspectives. In this paper, we propose a fully automated approach for diagnosing correctness of semantic workflow models in which the semantics of activities are specified with pre and postconditions. The control flow and dataflow perspectives of a semantic workflow are modeled in an integrated way using Artificial Intelligence techniques (Integer Programming and Constraint Programming). The approach has been implemented in the DiagFlow tool, which reads and diagnoses annotated XPDL models, using a state-of-the-art constraint solver as back end. Using this novel approach, complex semantic workflow models can be verified and diagnosed in an efficient way. |
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