Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
ASP-Based Declarative Process Mining
Authors: Francesco Chiariello, Fabrizio Maria Maggi, Fabio Patrizi5539-5547
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The contributions of the work include an ASP encoding schema for the three problems, their solution, and experiments showing the feasibility of the approach. Using the state-of-the-art solver Clingo1 (Gebser et al. 2019), we experimentally compare our approach against existing ones for Log Generation and Conformance Checking, and show effectiveness of the approach for Query Checking in a data-aware setting. |
| Researcher Affiliation | Academia | 1 DIAG Sapienza University of Rome, Italy 2 KRDB Free University of Bozen-Bolzano, Italy |
| Pseudocode | No | The paper provides examples of ASP code snippets (Example 3) to illustrate the encoding, but these are not presented as formal pseudocode or algorithm blocks with specific labels like 'Algorithm 1'. |
| Open Source Code | Yes | Source code, declarative models and event logs used in the experiments are available at https://github.com/fracchiariello/process-mining-ASP. |
| Open Datasets | Yes | The real life logs used in the experiments are taken from the collection available at https://data.4tu.nl/. |
| Dataset Splits | No | The paper mentions generating logs of certain lengths (e.g., '10000 traces (of length from 10 to 30)') and using synthetic models, but does not specify train, validation, or test splits for these datasets or for the real-life logs. |
| Hardware Specification | Yes | The experiments have been carried out on a standard laptop Dell XPS 15 with an Intel i7 processor and 16GB of RAM. |
| Software Dependencies | No | The paper mentions software like 'Clingo', 'Alloy', 'Declare Analyzer', and 'Lydia', but it does not specify explicit version numbers for these software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | No | The paper describes characteristics of the input data used for experiments (e.g., 'synthetic models containing 3, 5, 7, and 10 constraints', 'logs with 10000 traces (of length from 10 to 30)'). However, it does not provide specific experimental setup details such as hyperparameters, optimization settings, or other system-level configurations for running the ASP programs. |