Mining Process Task Post-Conditions

Santiputri, Metta; Ghose, Aditya; Dam, Hoa Khanh; Wen, Xiong

Mining Process Task Post-Conditions

dc.contributor.author	Santiputri, Metta
dc.contributor.author	Ghose, Aditya
dc.contributor.author	Dam, Hoa Khanh
dc.contributor.author	Wen, Xiong
dc.date.accessioned	2023-06-28T07:22:30Z
dc.date.available	2023-06-28T07:22:30Z
dc.date.issued	2015-12-08
dc.description.abstrak	A large and growing body of work explores the use of semantic annotation of business process designs, but these annotations can be difficult and expensive to acquire. This paper presents a data-driven approach to mining these annotations (and specifically post-conditions) from event logs in process execution histories which describe both task execution events (typically contained in process logs) and state update events (which we record in effect logs). We present an empirical evaluation, which suggests that the approach provides generally reliable results.	en_US
dc.identifier.citation	Santiputri, M., Ghose, A.K., Dam, H.K., Wen, X. (2015). Mining Process Task Post-Conditions. In: Johannesson, P., Lee, M., Liddle, S., Opdahl, A., Pastor López, Ó. (eds) Conceptual Modeling. ER 2015. Lecture Notes in Computer Science(), vol 9381. Springer, Cham. https://doi.org/10.1007/978-3-319-25264-3_38	en_US
dc.identifier.isbn	978-3-319-25263-6
dc.identifier.other	https://doi.org/10.1007/978-3-319-25264-3_38
dc.identifier.uri	https://repository.polibatam.ac.id/xmlui/handle/123456789/1724
dc.language.iso	en	en_US
dc.publisher	Springer	en_US
dc.relation.ispartofseries	Lecture Notes in Computer Science;volume 9381
dc.relation.ispartofseries	Proceedings of the 34th International Conference on Conceptual Modeling (ER 2015);
dc.subject	Semantic annotation	en_US
dc.subject	Data-driven	en_US
dc.subject	Process logs	en_US
dc.subject	Effect logs	en_US
dc.title	Mining Process Task Post-Conditions	en_US
dc.type	Book chapter	en_US