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Reliable Predictive Process Monitoring

Commonwealth Scientific and Industrial Research Organisation
Klinkmueller, Christopher ; Van Beest, Nick ; Weber, Ingo
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/08/5acff82350c74&rft.title=Reliable Predictive Process Monitoring&rft.identifier=https://doi.org/10.4225/08/5acff82350c74&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=This collection comprises the two synthetic datasets for the assessment of the reliability of predictive process monitoring techniques used in Klinkmüller, C., van Beest, N., Weber, I.: Towards Reliable Predictive Process Monitoring. CAiSE Forum, 2018.\n\nThe two datasets are provided as XES-files. For more information on the Extensible Event Stream (XES) standard see http://www.xes-standard.org. \n\nThe classes for each traces are captured via the concept:name sub-element. Additionally, for each trace the classifiableFrom sub-element records the minimum number of events that must be observed for the trace to be classifiable. More information regarding the notion of classifiability is provided in the paper.\nLineage: The data was automatically generated by a Java program that was developed and configured by the authors of the respective paper.&rft.creator=Klinkmueller, Christopher &rft.creator=Van Beest, Nick &rft.creator=Weber, Ingo &rft.date=2018&rft.edition=v1&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2018.&rft_subject=Behavioral classification&rft_subject=predictive process monitoring&rft_subject=process mining&rft_subject=machine learning&rft_subject=Information systems organisation and management&rft_subject=Information systems&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2018.

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This collection comprises the two synthetic datasets for the assessment of the reliability of predictive process monitoring techniques used in Klinkmüller, C., van Beest, N., Weber, I.: Towards Reliable Predictive Process Monitoring. CAiSE Forum, 2018.

The two datasets are provided as XES-files. For more information on the Extensible Event Stream (XES) standard see http://www.xes-standard.org.

The classes for each traces are captured via the "concept:name" sub-element. Additionally, for each trace the "classifiableFrom" sub-element records the minimum number of events that must be observed for the trace to be classifiable. More information regarding the notion of classifiability is provided in the paper.
Lineage: The data was automatically generated by a Java program that was developed and configured by the authors of the respective paper.

Available: 2018-04-13

Subjects
Behavioral classification | Information and Computing Sciences | Information Systems | Information Systems Organisation and Management | machine learning | predictive process monitoring | process mining |

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