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Unbiased, Fine-Grained Description of Processes Performance from Event Data · Study

WorkUnbiased, Fine-Grained Description of Processes Performance from Event Data

Vadim Denisov2018

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Workgreen2018

Unbiased, Fine-Grained Description of Processes Performance from Event Data

Vadim Denisov · Dirk Fahland · Wil M. P. van der Aalst

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

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Business Process Modeling and Analysis Data Mining Algorithms and Applications Software System Performance and Reliability

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References

2016Process Mining 2012Replaying history on process models for conformance checking and performance analysis 2017Predictive Business Process Monitoring with LSTM Neural Networks 2010Time prediction based on process mining 2008Discovering simulation models 2018Time and activity sequence prediction of business process instances 2008Cycle Time Prediction: When Will This Case Finally Be Finished?2010Beyond Process Mining: From the Past to Present and Future

Cited by

2022On the adoption of blockchain for business process monitoring 2022Action-oriented process mining: bridging the gap between insights and actions 2022Process Mining: A Guide for Practitioners 2023Defining and visualizing process execution variants from partially ordered event data 2020Detecting System-Level Behavior Leading To Dynamic Bottlenecks 2019Performance Mining for Batch Processing Using the Performance Spectrum

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.

2017Intra and Inter-case Features in Predictive Process Monitoring: A Tale of Two Dimensionsother · 6 shared references 2020An extended model for remaining time prediction in manufacturing systems using process miningarticle · 6 shared references 2019From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoringarticle · 6 shared references 2020An interdisciplinary comparison of sequence modeling methods for next-element predictionarticle · 6 shared references 2023A Direct Data Aware LSTM Neural Network Architecture for Complete Remaining Trace and Runtime Predictionarticle · 6 shared references 2018Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion timesarticle · 6 shared references

Details

Year2018

Typeother

Published2018-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations48

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

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doi10.1007/978-3-319-98648-7_9

openalex_workW2886688976

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2020Repairing Event Logs with Missing Events to Support Performance Analysis of Systems with Shared Resources

2020Identifying and Reducing Errors in Remaining Time Prediction due to Inter-Case Dynamics