Relevance-guided Sampling for Conformance Checking

This repository contains an implementation, as well as the used experimental result data for the article "Sampling What Matters: Relevance-guided Sampling of Event Logs".

The proposed sampling procedures guide the selection of traces to be included in a sample by learning which features of a trace correlate with trace properties of interest (so far we used deviating traces as interesting).

How to use

Using the command line interface

To generate a sample of the log from the command line, run:

python3 Logsampling.py <-index_file INDEX_FILE> <feature|behavioural> <sample size> <log_file> <model_file>

The parameters are

• algorithm - the indexing type to use. Can be either "feature" or "behavioural"
• sample size - the desired sample size
• log_file - the provided log file, in .xes format
• model_file - the model file used during sampling, in .pnml format
• index_file - (Optional) the index file, containing the features to consider during sampling

Invoking the classes from code

Alternatively, you can use the sampling procedures directly in your own project. For this, you first need to create the desired sampler:

Feature-based index

sampler = FeatureGuidedLogSampler(log, index_file=index_file)

Sequence-based index

sampler = SequenceGuidedLogSampler(log, batch_size=5, index_file=index_file)

Then, you may generate a sample by calling the following line:

sample = sampler.construct_sample(log, model, initial_marking, final_marking, sample_size)

Index files

By providing an index-file, you can specify, what log attributes may be considered as relevant during the generation of the sample. In this xml-file you can specify the trace-level attributes, event-level attributes, k-gram lengths and potential preceeding discreditization steps, that comprise the feature index, that guides the sampling procedure.

The structure of the .xml-file is as follows

<index>
  <k>3</k>                                        #use activity k-grams of length 3
  <discretize>
    <attribute>attribute a</attribute>            #discretize trace-level attribute 'a' using a bucket size of 10
    <trace-level>true</trace-level>
    <bucket_width>10</bucket_width>
  </discretize>
    <discretize>
    <attribute>attribute b</attribute>             #discretize event-level attribute 'b' using a bucket size of 5
    <trace-level>false</trace-level>
    <bucket_width>5</bucket_width>
  </discretize>
  <discretize>                                    #additional attributes that need discretization
    ...
  </discretize>
  
  <features>
    <trace-level>
      <attribute>attribute a</attribute>          #incorporate trace-level attribue 'a'
      ...                                         #additional trace-level attributes to incorporate
    </trace-level>
    <event-level>
      <attribute>attribute b</attribute>          #incorporate event-level attribue 'b'
      ...                                         #additional event-level attributes to incorporate
    </event-level>
  </features>
</index>

If no index file is provided, all features without discretization, as well as 3-grams, are considered.

Evaluation scripts and data

Under results, we provide the scripts used for experimental evaluation(eval.py) and plot generation(generate_plots.py), as well as the .csv-files and plots, that were generated using these scripts and used for the article.

Citing this work

If you use this repository, please cite the article:

@INPROCEEDINGS{9576875,
  author={Kabierski, Martin and 
    Nguyen, Hoang Lam and 
    Grunske, Lars and 
    Weidlich, Matthias},
  booktitle={2021 3rd International Conference on Process Mining (ICPM)}, 
  title={Sampling What Matters: Relevance-guided Sampling of Event Logs}, 
  year={2021},
  volume={},
  number={},
  pages={64-71},
  doi={10.1109/ICPM53251.2021.9576875}}
}