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Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models · Study

WorkOnline Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models

Fabrizio Maria Maggi2013

Work2013

Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models

Fabrizio Maria Maggi · Andrea Burattin · Marta Cimitile · Alessandro Sperduti

50 citations·0/0 files stored·Lecture notes in computer science

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Business Process Modeling and Analysis Formal Methods in Verification Data Mining Algorithms and Applications

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References

2011Process Mining 2011Process Mining: Discovery, Conformance and Enhancement of Business Processes 2008Business Process Management Workshops 2009Declarative workflows: Balancing between flexibility and support 2011User-guided discovery of declarative process models 2009Robust Process Discovery with Artificial Negative Events 2012Efficient Discovery of Understandable Declarative Process Models from Event Logs 2012Imperative versus Declarative Process Modeling Languages: An Empirical Investigation

Cited by

2020Evaluation Goals for Online Process Mining: A Concept Drift Perspective 2022How do I update my model? On the resilience of Predictive Process Monitoring models to change 2014Log-Based Understanding of Business Processes through Temporal Logic Query Checking 2020Analyzing Process Concept Drifts Based on Sensor Event Streams During Runtime 2015PLG2: Multiperspective Processes Randomization and Simulation for Online and Offline Settings

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Details

Year2013

Typeother

Published2013-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations50

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

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doi10.1007/978-3-642-41030-7_7

openalex_workW124909259

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2018

Process Histories - Detecting and Representing Concept Drifts Based on Event Streams

2018On the relevance of a business constraint to an event log

2015Lights, Camera, Action! Business Process Movies for Online Process Discovery