Titlebook: Business Process Management; 13th International C Hamid Reza Motahari-Nezhad,Jan Recker,Matthias Wei Conference proceedings 2015 Springer I

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案发地点

Inspection Coming Due! How to Determine the Service Interval of Your Processes!they do not have enough resources to deeply inspect all processes simultaneously. Nor would this be reasonable from a process performance point of view. Process portfolio managers therefore require guidance on how to determine the service interval of their processes, i.e., when they should analyze w

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Data-Driven Performance Analysis of Scheduled Processesrmance analysis do not take both queueing semantics and the process perspective into account. In this work, we address this gap by developing a novel method for utilizing rich process logs to analyze performance of scheduled processes. The proposed method combines simulation, queueing analytics, and

forestry

occult

Decimate

Detecting Inconsistencies Between Process Models and Textual Descriptionst is not unusual to find within an organization descriptions of the same business processes in both modes. When considering that hundreds of such descriptions may be in use in a particular organization by dozens of people, using a variety of editors, there is a clear risk that such models become mis

Control-Group

Mining Invisible Tasks in Non-free-choice Constructs in the life-cycle of a process-aware information system. However, in a decade of process discovery research, the relevant algorithms are known to have strong limitations in several dimensions. . and . are two important special structures in a process model. Mining invisible tasks involved in non-fr

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Incorporating Negative Information in Process Discovery angles. Most of the contributions consider the extraction of a model as a . problem where only positive information is available. In this paper we present a fresh look at process discovery where also negative information can be taken into account. This feature may be crucial for deriving process mo

摇摆

CREEK

Avoiding Over-Fitting in ILP-Based Process Discoveryg process discovery techniques is the ILP-based process discovery algorithm. The algorithm is able to unravel complex process structures and provides formal guarantees w.r.t. the model discovered, e.g., the algorithm guarantees that a discovered model describes all behavior present in the event log.