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Published in: Journal on Data Semantics 2/2019

16-11-2018 | Original Article

Ant-Colony Optimisation for Path Recommendation in Business Process Execution

Author: Marco Comuzzi

Published in: Journal on Data Semantics | Issue 2/2019

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Abstract

In business process management, operational support concerns methods and tools to support users during the execution of business processes. One possible way of supporting users is to suggest the optimal way to complete the execution of a business process instance given the set of activities executed thus far and a notion of utility associated with the execution of possible remaining activities. This problem goes also under the label of process navigation. This paper proposes a novel technique to implement process navigation based on the innovative abstraction of business process models as a restricted class of directed hypergraphs, i.e. WF-hypergraphs. In our approach, workflow net process models are first transformed into WF-hypergraphs. Using this abstraction, finding the optimal way to complete a business process becomes a generalised hypergraph shortest path problem, which is NP-hard. To solve this problem, we propose a solution based on the ant-colony meta-heuristic specifically customised to the case of hypergraph traversal. The paper presents an experimental evaluation of the proposed optimisation heuristic and discusses how the proposed approach can be integrated into modern business process management systems.

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Footnotes
1
Note that, with an abuse of notation, we identify the nodes created in step 1 using the corresponding labels in PN.
 
2
For a parallel block, the execution time is equal to the longest expected execution time of a branch in the block; for a conditional block, the execution time is equal to the sum of the expected execution time of activities in a block weighted by their respective probability of execution.
 
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Metadata
Title
Ant-Colony Optimisation for Path Recommendation in Business Process Execution
Author
Marco Comuzzi
Publication date
16-11-2018
Publisher
Springer Berlin Heidelberg
Published in
Journal on Data Semantics / Issue 2/2019
Print ISSN: 1861-2032
Electronic ISSN: 1861-2040
DOI
https://doi.org/10.1007/s13740-018-0099-x

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