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Lambda Architecture for Anomaly Detection in Online Process Mining Using Autoencoders · Study

WorkLambda Architecture for Anomaly Detection in Online Process Mining Using Autoencoders

Philippe Krajsic2020

Work2020

Lambda Architecture for Anomaly Detection in Online Process Mining Using Autoencoders

Philippe Krajsic · Bogdan Franczyk

13 citations·0/0 files stored·Communications in computer and information science

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Business Process Modeling and Analysis Software System Performance and Reliability Anomaly Detection Techniques and Applications

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References

2004Workflow mining: discovering process models from event logs 2016Process Mining 2016Process Mining: Data Science in Action 2013Discovering Block-Structured Process Models from Event Logs - A Constructive Approach 2016Filtering Out Infrequent Behavior from Business Process Event Logs 2018Analyzing business process anomalies using autoencoders 2017Detecting sudden and gradual drifts in business processes from execution traces 2017Filtering out infrequent behavior from business process event logs

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2019Detection and removal of infrequent behavior from event streams of business processesarticle · 10 shared references 2018Repairing Outlier Behaviour in Event Logsother · 9 shared references 2018Filtering Spurious Events from Event Streams of Business Processesother · 8 shared references 2018Applying Sequence Mining for Outlier Detection in Process Miningother · 8 shared references 2019Filtering Out Noise Logs for Process Modelling Based on Event Dependencyother · 8 shared references 2018Discovering more precise process models from event logs by filtering out chaotic activitiesarticle · 7 shared references

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Year2020

Typeother

Published2020-01-01

VenueCommunications in computer and information science

ISSN-L1865-0929

Languageen

Citations13

Files0 public / 0

ShelfBusiness Process Modeling and Analysis

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doi10.1007/978-3-030-63119-2_47

openalex_workW3110502136

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