Prolothar Process Discovery

Algorithms to discover process behavior from data mining on sequential data such as process logs.

Based on the publication

Boris Wiegand, Dietrich Klakow, and Jilles Vreeken. Mining easily understandable models from complex event logs. In: Proceedings of the SIAM International Conference on Data Mining (SDM), Virtual Event. 2021, pp. 244-252.

Prerequisites

Python 3.11+

Usage

If you want to run the algorithms on your own data, follow the steps below.

Installing

pip install prolothar-process-discovery

Creating or reading an EventLog

Option 1: you can create an EventLog from a pandas dataframe

# 1) there must be a header line
# 2) each line belongs to one event
# 3) there is one column containing the case ID
# 4) there is one column containing the activity name of the event
# 5) there can be columns for trace and event attributes
import pandas as pd
eventlog = EventLog.create_from_pandas_df(
      pd.read_csv('path/to/eventlog.csv', delimiter=','),
      'CaseId', 'Activity',
      trace_attribute_columns=['Customer'],
      event_attribute_columns=['Duration']
)

Option 2: you can create an EventLog from .xes with the help of the pm4py package

from pm4py.objects.log.importer.xes import importer as xes_import_factory
import prolothar_common.pm4py_utils as pm4py_utils
xes = xes_import_factory.apply('path/to/eventlog.xes.gz')
eventlog = pm4py_utils.convert_pm4py_log(xes)

Option 3: you can create an EventLog manually

from prolothar_common.models.eventlog import EventLog, Trace, Event
eventlog = EventLog()
#case ID (0 in the example) can be any hashable type, e.g. int or string. must be unique.
eventlog.add_trace(Trace(0, [
      Event('start computer', attributes={'user': 'alice'}),
      Event('drink coffee', attributes={'milk': 'yes', 'grams_of_sugar': 5}),
]))

Discovering a PatternGraph

from prolothar_process_discovery.discovery import Proseqo
from prolothar_process_discovery.discovery import ProSimple
from prolothar_process_discovery.discovery.proseqo.pattern_dfg import PatternDfg

directly_follows_graph = PatternDfg.create_from_event_log(eventlog)

pattern_graph = Proseqo().mine_dfg(eventlog, directly_follows_graph, verbose=True)
pattern_graph.plot()

pattern_graph = ProSimple().mine_dfg(eventlog, directly_follows_graph, verbose=True)
# we can also plot to a file
pattern_graph.plot(filepath='path/to/your/file', filetype: str='pdf', view=False)

If you get an error stating that parameter "last_covered_activity" is unexpectedly of type "None", add a common start and a common end activity to all traces:

log.add_start_activity_to_every_trace('START')
log.add_end_activity_to_every_trace('END')

Development

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Additional Prerequisites

• make (optional)

Running the tests

make test

Deployment

1. Change the version in version.txt
2. Build and publish the package on pypi by

make clean_package
make package && make publish

3. Create and push a tag for this version by

git tag -a [version] -m "describe this version"
git push --tags

Versioning

We use SemVer for versioning.

Authors

If you have any questions, feel free to ask one of our authors:

• Boris Wiegand - boris.wiegand@stahl-holding-saar.de