A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis

Cabezas-Quinto, Jessica Janina; Vidal-Silva, Cristian; Serrano-Malebrán, Jorge; Márquez, Nicolás

doi:10.3390/data10090139

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Open AccessArticle

A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis

by

Jessica Janina Cabezas-Quinto

¹,

Cristian Vidal-Silva

^2,*

,

Jorge Serrano-Malebrán

^3,*

and

Nicolás Márquez

^4,*

¹

Facultad de Ciencias e Ingeniería, Universidad Estatal de Milagro, Milagro 090103, Ecuador

²

Departamento de Visualización Interactiva y Realidad Virtual, Facultad de Ingeniería, Universidad de Talca, Av. Lircay S/N, Talca 3460000, Chile

³

Facultad de Ingeniería y Negocios, Universidad de las Américas, Av. Manuel Montt 948 Providencia, Santiago 7500000, Chile

⁴

Escuela de Ingeniería Comercial, Facultad de Economía y Negocios, Universidad Santo Tomás, Talca 3460000, Chile

^*

Authors to whom correspondence should be addressed.

Data 2025, 10(9), 139; https://doi.org/10.3390/data10090139

Submission received: 24 June 2025 / Revised: 6 August 2025 / Accepted: 26 August 2025 / Published: 29 August 2025

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Abstract

This article presents a dataset and experimental evaluation of a parallelized variant of Junker’s QuickXPlain algorithm, designed to efficiently compute minimal conflict sets in constraint-based diagnosis tasks. The dataset includes performance benchmarks, conflict traces, and solution metadata for a wide range of configurable diagnosis problems based on real-world and synthetic CSP instances. Our parallel variant leverages multicore architectures to reduce computation time while preserving the completeness and minimality guarantees of QuickXPlain. All evaluations were conducted using reproducible scripts and parameter configurations, enabling comparison across different algorithmic strategies. The provided dataset can be used to replicate experiments, analyze scalability under varying problem sizes, and serve as a baseline for future improvements in conflict explanation algorithms. The full dataset, codebase, and benchmarking scripts are openly available and documented to promote transparency and reusability in constraint-based diagnostic systems research.

Keywords: parallel computating; conflict detection; constraint satisfaction problems; diagnosis; QuickXPlain; benchmarking; reproducible evaluation; minimal conflict sets; open dataset

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MDPI and ACS Style

Cabezas-Quinto, J.J.; Vidal-Silva, C.; Serrano-Malebrán, J.; Márquez, N. A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis. Data 2025, 10, 139. https://doi.org/10.3390/data10090139

AMA Style

Cabezas-Quinto JJ, Vidal-Silva C, Serrano-Malebrán J, Márquez N. A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis. Data. 2025; 10(9):139. https://doi.org/10.3390/data10090139

Chicago/Turabian Style

Cabezas-Quinto, Jessica Janina, Cristian Vidal-Silva, Jorge Serrano-Malebrán, and Nicolás Márquez. 2025. "A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis" Data 10, no. 9: 139. https://doi.org/10.3390/data10090139

APA Style

Cabezas-Quinto, J. J., Vidal-Silva, C., Serrano-Malebrán, J., & Márquez, N. (2025). A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis. Data, 10(9), 139. https://doi.org/10.3390/data10090139

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A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis

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