We aim to reduce the size of event logs (from Process Mining) to p-traces, while minimizing the earth movers' distance (EMD) from the unsampled original event log.
This Github complements our paper and is composed of two main parts:
- Experiment. Contains the code that was run to benchmark various approaches (results reported in the paper). It also contains some graphs that could not fit the paper.
- Standalone. The standalone version offers a simple interface to apply our proposed iterative c-min sampler so that running our sampling technique is as easy as:
from standalone.CminSampler import CminSampler
seqs = [
['a','b','c'],
['a','b','c'],
['a','b','c'],
['a'],
['a'],
['a','b','b','c'],
]
sampler = CminSampler(3)
sampler.load_list(seqs)
print (sampler.sample())Output:
[0, 2, 4]It is suggesting that the sequences with index 0, 2, and 4 are the 3 best traces that summarizes the event logs initially composed of 6 traces.
It is also possible to load a Dataframe as follow:
from standalone.CminSampler import CminSampler
import pandas as pd
df = pd.read_csv('../data/data_sample.csv', sep=';')
sampler = CminSampler(6)
sampler.load_df(df, 'Incident ID', 'IncidentActivity_Type')Output:
['IM0000005', 'IM0000012', 'IM0000013', 'IM0000015', 'IM0000017', 'IM0000020']Suggesting that these are the case IDs that best represent the original dataset