Mining task post-conditions: Automating the acquisition of process semantics
| dc.contributor.author | Santiputri, Metta | |
| dc.contributor.author | Ghose, Aditya | |
| dc.contributor.author | Dam, Hoa Khanh | |
| dc.date.accessioned | 2023-06-28T03:52:08Z | |
| dc.date.available | 2023-06-28T03:52:08Z | |
| dc.date.issued | 2017-05 | |
| dc.description.abstrak | Semantic annotation of business process model in the business process designs has been addressed in a large and growing body of work, but these annotations can be difficult and expensive to acquire. This paper presents a data-driven approach to mining and validating these annotations (and specifically context-independent semantic annotations). We leverage event objects in process execution histories which describe both activity execution events (typically represented as process events) and state update events (represented as object state transition events). We present an empirical evaluation, which suggests that the approach provides generally reliable results. | en_US |
| dc.identifier.issn | 0169-023X | |
| dc.identifier.other | https://doi.org/10.1016/j.datak.2017.03.007 | |
| dc.identifier.uri | https://repository.polibatam.ac.id/xmlui/handle/123456789/1708 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier B.V. | en_US |
| dc.relation.ispartofseries | Data & Knowledge Engineering;Volume 109 | |
| dc.subject | Business process semantics | en_US |
| dc.subject | Mining post-conditions | en_US |
| dc.subject | Semantic annotation | en_US |
| dc.title | Mining task post-conditions: Automating the acquisition of process semantics | en_US |
| dc.type | Article | en_US |
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