Please use this identifier to cite or link to this item: https://idr.l4.nitk.ac.in/jspui/handle/123456789/7745
Full metadata record
DC FieldValueLanguage
dc.contributor.authorManoj, Kumar, M.V.
dc.contributor.authorThomas, L.
dc.contributor.authorAnnappa, B.
dc.date.accessioned2020-03-30T10:02:44Z-
dc.date.available2020-03-30T10:02:44Z-
dc.date.issued2017
dc.identifier.citationACM International Conference Proceeding Series, 2017, Vol.Part F127854, , pp.157-161en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7745-
dc.description.abstractIf the operational process is flexible, control flow discovery methods in process mining tend to produce Spaghetti (unstructured) models. Spaghetti models generally consist of large number of activities and paths. These models are unstructured, incomprehensible difficult to analyse, impossible to use during operational support and enhancement. Due The structural complexity of Spaghetti processes majority of techniques in process mining can not be applied on them. There is a at most necessity to design and develop methods for simplifying the structure of Spaghetti process to make them easily understandable and reusable. The methods proposed in this paper concentrates on offering the tools and techniques for analysing the Spaghetti process. The problems addressed in this paper are 1) converting the unstructured Spaghetti to structured and simplified Lasagna process, 2) identifying the list of possible, significant, and impossible paths of execution in Lasagna process. The proposed technique is verified and validated on real-life road traffic fine management event-log taken from standard repository. � 2017 ACM.en_US
dc.titleDistilling lasagna from spaghetti processesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.