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Code implementing an estimation framework and evaluation test scaffold for Stochastic Process Discovery, as described in "Burke, A, Leemans, S.J.J and Wynn, M. T. - Stochastic Process Discovery By Weight Estimation"

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spd_we

This project shows a technique for discovering Stochastic Petri Nets from event logs using process mining. It combines a number of established process mining discovery algorithms with weight estimators which capture a stochastic perspective. It also includes scaffolding code for experimental evaluation, and result files from our evaluation.

The estimation framework and evaluation is described in "Burke, A, Leemans, S.J.J and Wynn, M. T. (2021) - Stochastic Process Discovery By Weight Estimation", DOI 10.1007/978-3-030-72693-5_20. You can also see this short blog post.

Developer and Command Line Use

Running From Command Line

The test scaffold entry point is ModelRunner.java.

PPTEstimateRunner.java is a simple tool for applying estimated weights to process tree models, so long as the original discovery algorithm outputs a process tree.

Note that both ModelRunner and PPTEstimateRunner depend on a config file called instance.properties found in the config directory. Most of the behaviour is controlled from this file, including which estimators to use, directories for input event logs and models, and so on. The meaning of the properties is defined in ModelRunner.

The default instance.properties reads an example log file data/exercise1.xes, runs the Inductive miner on it, performs weight estimation, and calculates the Earth Movers' Distance using the tEMSC 0.8 measure.

XML model and result files are output to 'var/'; mrun_* files are calculations, osmodel_* files are PNML files with stochastic weights.

The reporting entry point is SPNDiscoverReporter.java.

Some scripts for running on Windows and Unix are in scripts/.

Example Output

2024-02-22 16:48:26,965 INFO  [main] spn_discover.ModelRunner - SPM model runner initializing
2024-02-22 16:48:26,969 INFO  [main] spn_discover.ModelRunner - Using data location data
2024-02-22 16:48:26,969 INFO  [main] spn_discover.ModelRunner - Using data files [exercise1.xes]
2024-02-22 16:48:26,974 INFO  [main] spn_discover.ModelRunner - Using classifier NAME
2024-02-22 16:48:26,988 INFO  [main] spn_discover.ModelRunner - Beginning run -- Inductive Miner -- exercise1.xes
...
2024-02-22 16:48:27,340 INFO  [main] spn_discover.ModelRunner - SPM model runner finished

Building

Requirements:

  • Java 8 (version 8 due to ProM required JDK version)
  • ant, ivy
  • lpsolve 5.5 (for alignments, install in ldlib)
  • ivy will download third party jars for fodina and prob-process-tree
  • Some miners require extra jars, see note in ldlib folder. This is needed when doing integrated miner+estimator runs from the command line.
  • R (for the R scripts used in reporting only)

To build:

ant resolve

ant test

To build a zip for distributing, eg to run from the Windows command line or on Unix: ant makezip

ProM Users

Installation

Unfortunately, due to limitations in the latest versions of ProM connected to classloaders and later Java versions, it appears distributing plugins via simple jar file downloads may no longer be possible. This is based on testing with ProM 6.10 and 6.11 in December 2023 and January 2024, using both this estimator plugin and the Fodina plugin. Any communication on fixes for this are welcome. A new drop-in jar and the instructions from the last working ProM version are left below to help those developing a workaround.

The jar file spndiscover-x.y.z.jar is provided with most releases. This can be downloaded and placed in a plugin folder on the ProM classpath.

To add an unregistered ProM plugin:

  1. Create a new subfolder in your ProM installation directory called plugins. It must be a separate directory to the existing lib directory.
  2. Add all required jars for your plugin to the plugins directory. Do not include ProM libraries or plugins provided with the ProM distribution.
  3. Edit the file ProMxyz.bat to include plugins in the classpath.
  4. Run ProM.

This essentially the same installation process to Fodina.

Running Plugins

Once installed, the plugins are

  • Mine Stochastic Petri net with estimators
    • User can selects an estimator and classifier through the GUI.
  • Mine Stochastic Petri net from log with estimator
    • Uses a default miner and estimator to produce a GSPN directly from an event log.

Results

Result files from experiments performed on this framework are in results.

About

Code implementing an estimation framework and evaluation test scaffold for Stochastic Process Discovery, as described in "Burke, A, Leemans, S.J.J and Wynn, M. T. - Stochastic Process Discovery By Weight Estimation"

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