Requirements

The requirements to run the experiments are listed in the requirements.txt file. Currently, you need to install Declare4Py separately - you can follow the package mantainers' instructions at this page. Or if you trust me:

git clone --recurse-submodules https://github.com/kr2023-6949/experiments.git && cd experiments
python3.10 -m venv venv
source venv/bin/activate
pip3 install declare4py/dist/declare4py-1.0.0.tar.gz
pip3 install -r requirements.txt

Data Generation

Details (code) about data generation are available in the cformule.dataset_generation.generate_cformulae Python package. By running this script:

python3.10 generate_data.py data/sepsis.xes data/

all necessary formulae to repeat the experiments is generated.

cf_*.lp files are needed to run the conformance checking, trace clustering and discriminative discovery experiments, while the qc_*.lp files are needed for the query checking experiment.

The ones used in the paper's experimental section are already in the folder.

ASP Encodings

The ASP encodings are stored in the cformulae/application/encodings folder. Facts are injected at runtime when input log gets parsed, and are executed through the clingo Python API. For further details check conformance_checking, query_checking, trace_clustering, discriminative_discovery functions in the cformulae.application subpackage.

The procedures to evaluate constraints' via clingo's @-terms are defined in the LogContext class in the cformulae.backend.template_backend Python module. The experiments were performed using only Declare-based templates (for ease of generation), these are automatically loaded.

The collection of available Declare constraints (and their POSIX's re definition) can be found in the cformulae/backend/declare/minerful_templates.txt file.

Running the experiments

In order to run the experiments in the paper, you can use the following example scripts. The defaults for optional arguments are the configurations used in the paper.

Conformance checking

python3.10 cf.py data/sepsis.xes formulae.lp -o formulae.lp.output

This runs the conformance checking task on the sepsis.xes log, using the formulae defined in formulae.lp file. Call with -h flag to check optional arguments.

Query checking

python3.10 qc.py data/sepsis.xes formulae.lp -o formulae.lp.output

This runs the query checking task on the sepsis.xes log, using the non-ground formulae and variable domains defined in formulae.lp file. Call with -h flag to check optional arguments.

Trace clustering

python3.10 tc.py data/sepsis.xes formulae.lp.output [num_partitions]

This runs the trace clustering task on the sepsis.xes log, where formulae.lp.output is the output of cf.py on the formulae.lp set of formulae (on the same sepsis.xes log). Call with -h flag to check optional arguments.

Warnings about rejects/2 (accepts/2) not appearing in the head of any rule are due to the fact that the formulae in formulae.lp do not reject (accept) any control-flow variable in the log sepsis.xes.

Discriminative discovery

python3.10 tc.py data/sepsis.xes formulae.lp.output [num_partitions]

This runs the discriminative discovery task on the sepsis.xes log, where formulae.lp.output is the output of cf.py on the formulae.lp set of formulae (on the same sepsis.xes log). Call with -h flag to check optional arguments. This is only a proof-of-concept and randomly generates labels for the traces in the log.

Warnings about rejects/2 (accepts/2) not appearing in the head of any rule are due to the fact that the formulae in formulae.lp do not reject (accept) any control-flow variable in the log sepsis.xes.

Parallel Execution w/ GNU Parallel

The following scripts can be used to run the tasks in parallel using GNU Parallel:

parallel -j T --progress --results joblogs/cf_X.csv python3.10 cf.py data/sepsis.xes {1} ">" {1}.output ::: $(ls data/formulae_X/cf_*.lp)
parallel -j T --progress --results joblogs/dd_X.csv python3.10 dd.py data/sepsis.xes {1} {2} ::: $(ls data/formulae_X/cf_*.lp.output) ::: 2 4 6
parallel -j T --progress --results joblogs/tc_X.csv python3.10 tc.py data/sepsis.xes {1} {2} ::: $(ls data/formulae_X/cf_*.lp.output) ::: 2 4 6
parallel -j T --progress --results joblogs/qc_X.csv python3.10 qc.py data/sepsis.xes {1} ::: $(ls data/formulae_X/qc_*.lp)

where T is the number of jobs to be computed in parallel and X = 1 ... 6 select a subset of the formulae in the data/ folder. The figures.py script generates the plots - this requires some extra libraries like matplotlib, pandas and seaborn. The folder joblogs contains the output of the executions used in the paper.