%0 DATA
%A Philipp J., Meyer
%A Javier, Esparza
%A Philip, Offtermatt
%D 2019
%T Artifact and instructions to generate experimental results for TACAS 2019 paper: Computing the Expected Execution Time of Probabilistic Workflow Nets
%U https://springernature.figshare.com/articles/dataset/Artifact_and_instructions_to_generate_experimental_results_for_TACAS_2019_paper_Computing_the_Expected_Execution_Time_of_Probabilistic_Workflow_Nets/7831781
%R 10.6084/m9.figshare.7831781.v1
%2 https://springernature.figshare.com/ndownloader/files/14570534
%K Workflow Petri Nets
%K Workflow Graphs
%K Business Process Modeling
%K Expected Time
%K Free-choice Petri Nets
%K Confusion-free Petri Nets
%K Probabilistic Workflow Nets
%K #P-hardness
%K TACAS 2019
%X This is the artifact accompanying the paper "Computing the Expected
Execution Time of Probabilistic Workflow Nets", accepted for TACAS 2019.
Timed Probabilistic Workflow Nets (TPWN) are a model for business
process modeling extended with time and probabilistic information. In
the paper, we have shown that computing the excepted execution time of a
TPWN is #P-hard, even for simple net structures. We then developed an
exponential time algorithm to compute the expected execution time, and
evaluated it on a set of real-word workflow net benchmarks. As a result,
we obtained that for most nets, the time can be computed efficiently
despite the theoretical hardness.

This artifact can be used to
reproduce the results given in Section 6 "Experimental evaluation" of
the paper. It includes the workflow and process mining workbench ProM
together with the plugin we implemented to compute the expected
execution time. Further, this artifact contains a set of 642 workflow
nets from IBM, annotated with 3 sets of random times and probabilities,
and scripts to compute the expected execution time of all nets and
aggregate the results. This can be used to validate the results in Table
1. Also, the workflow net from the BPI Challenge shown in Fig. 4 of the
paper is included, in a deterministic and probabilistic version. This
net can be analyzed using ProM to validate the results in Table 2.