Computing alignments of well-formed process models using local search
The alignment of observed and modeled behavior is an essential element for organizations, since it opens the door for conformance checking and enhancement of processes. The state-of-the-art technique for computing alignments has exponential time and space complexity, hindering its applicability for...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/331006 |
| Acceso en línea: | https://hdl.handle.net/2117/331006 https://dx.doi.org/10.1145/3394056 |
| Access Level: | acceso abierto |
| Palabra clave: | Data mining Process mining Conformance checking Process models Event logs Mineria de dades Àrees temàtiques de la UPC::Informàtica::Enginyeria del software |
| Sumario: | The alignment of observed and modeled behavior is an essential element for organizations, since it opens the door for conformance checking and enhancement of processes. The state-of-the-art technique for computing alignments has exponential time and space complexity, hindering its applicability for medium and large instances. In this article, a novel approach is presented to tackle the challenge of computing an alignment for large-problem instances that correspond to well-formed process models. Given an observed trace, first it uses a novel replay technique to find an initial candidate trace in the model. Then a local search framework is applied to try to improve the alignment until no further improvement is possible. The implementation of the presented technique reveals a magnificent reduction both in computation time and in memory usage. Moreover, although the proposed technique does not guarantee the derivation of an alignment with minimal cost, the experiments show that in practice the quality of the obtained solutions is close to optimal. |
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