Titlebook: Business Process Management; 18th International C Dirk Fahland,Chiara Ghidini,Marlon Dumas Conference proceedings 2020 Springer Nature Swit

文艺

theta-waves

Online Process Monitoring Using Incremental State-Space Expansion: An Exact Algorithmhes for monitoring the correctness of the execution of running processes have been developed in the area of process mining, i.e., online conformance checking. The advantages of monitoring a process’ conformity during its execution are clear, i.e., deviations are detected as soon as they occur and co

渐强

Looking for Meaning: Discovering Action-Response-Effect Patterns in Business Processesof process improvement is how response s to an event (action) result in desired or undesired outcomes (effects). From a process perspective, this requires understanding the action-response patterns that occur. Current discovery techniques do not allow organizations to gain such insights. In this pap

HUSH

Extracting Annotations from Textual Descriptions of Processesnel to understand the processes, specially for those ones that cannot interpret formal descriptions like BPMN or Petri nets. In this paper we present a technique based on Natural Language Processing and a query language for tree-based patterns, that extracts annotations describing key process elemen

Conquest

Analyzing Process Concept Drifts Based on Sensor Event Streams During Runtimeg., deviations in sensor event streams such as warehouse temperature in manufacturing or blood pressure in health care. Deviations in the process behavior during runtime can be detected from process event streams as so called concept drifts. Existing work has focused on concept drift detection so fa

heterodox

Aggrandize

Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Predi, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture,

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大暴雨

Process Minding: Closing the Big Data Gaps process mining along modern data life cycle, highlighting the challenges and suggesting directions in which data science disciplines (., machine learning) may interact with a renewed process mining agenda.