{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T22:21:07Z","timestamp":1776982867443,"version":"3.51.4"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>This article presents a dataset and experimental evaluation of a parallelized variant of Junker\u2019s 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.<\/jats:p>","DOI":"10.3390\/data10090139","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T12:25:57Z","timestamp":1756470357000},"page":"139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Dataset and Experimental Evaluation of a Parallel Conflict Detection Solution for Model-Based Diagnosis"],"prefix":"10.3390","volume":"10","author":[{"given":"Jessica Janina","family":"Cabezas-Quinto","sequence":"first","affiliation":[{"name":"Facultad de Ciencias e Ingenier\u00eda, Universidad Estatal de Milagro, Milagro 090103, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1600-3447","authenticated-orcid":false,"given":"Cristian","family":"Vidal-Silva","sequence":"additional","affiliation":[{"name":"Departamento de Visualizaci\u00f3n Interactiva y Realidad Virtual, Facultad de Ingenier\u00eda, Universidad de Talca, Av. Lircay S\/N, Talca 3460000, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6932-1556","authenticated-orcid":false,"given":"Jorge","family":"Serrano-Malebr\u00e1n","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda y Negocios, Universidad de las Am\u00e9ricas, Av. Manuel Montt 948 Providencia, Santiago 7500000, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2371-8253","authenticated-orcid":false,"given":"Nicol\u00e1s","family":"M\u00e1rquez","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Comercial, Facultad de Econom\u00eda y Negocios, Universidad Santo Tom\u00e1s, Talca 3460000, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/0004-3702(87)90062-2","article-title":"A Theory of Diagnosis from First Principles","volume":"32","author":"Reiter","year":"1987","journal-title":"Artif. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0004-3702(87)90063-4","article-title":"Diagnosing multiple faults","volume":"32","author":"Williams","year":"1987","journal-title":"Artif. Intell."},{"key":"ref_3","unstructured":"Junker, U. (2024, January 25\u201329). QUICKXPLAIN: Preferred explanations and relaxations for over-constrained problems. Proceedings of the 19th National Conference on Artificial Intelligence (AAAI-04), San Jose, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1007\/s40747-021-00613-5","article-title":"Knowledge representation for explainable artificial intelligence","volume":"8","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Felfernig, A., Reiterer, S., Reinfrank, F.C., Ninaus, G., and Jeran, M. (2014). Conflict Detection and Diagnosis in Configuration. Knowledge-Based Configuration: From Research to Business Cases, Elsevier B.V.. [1st ed.].","DOI":"10.1016\/B978-0-12-415817-7.00007-4"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Felfernig, A., Friedrich, G., Jannach, D., and Zanker, M. (2011). Constraint-Based Recommender Systems. Recommender Systems Handbook, Springer.","DOI":"10.1007\/978-0-387-85820-3_6"},{"key":"ref_7","first-page":"93","article-title":"A Survey of Tools for Knowledge Base Debugging","volume":"27","author":"Baumeister","year":"2013","journal-title":"KI\u2014K\u00fcNstliche Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dev Gupta, S., Gen\u00e7, B., and O\u2019Sullivan, B. (2021, January 19\u201326). Explanation in Constraint Satisfaction: A Survey. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021), Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/601"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6185","DOI":"10.1007\/s10462-022-10149-w","article-title":"A formal proof and simple explanation of the QuickXPlain algorithm","volume":"55","author":"Rodler","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s10844-021-00675-4","article-title":"Explanations for over-constrained problems using QuickXPlain with speculative executions","volume":"57","author":"Vidal","year":"2021","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_11","first-page":"127","article-title":"Model-based Diagnosis Meets Error Diagnosis in Logic Programs","volume":"Volume 749","author":"Fritzson","year":"1993","journal-title":"Proceedings of the Automated and Algorithmic Debugging\u2014AADEBUG 1993"},{"key":"ref_12","first-page":"326","article-title":"Finding Minimal Unsatisfiable Cores of Declarative Specifications","volume":"Volume 5014","author":"Cuellar","year":"2008","journal-title":"Proceedings of the FM 2008: Formal Methods"},{"key":"ref_13","first-page":"672","article-title":"Understanding, Improving and Parallelizing MUS Finding Using Model Rotation","volume":"Volume 7514","author":"Milano","year":"2012","journal-title":"Proceedings of the Principles and Practice of Constraint Programming\u2014CP 2012"},{"key":"ref_14","first-page":"2893","article-title":"Speculative parallelism for irregular programs using optimistic thread scheduling","volume":"31","author":"Ahn","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_15","first-page":"457","article-title":"A Parallelized Variant of Junker\u2019s QuickXPlain Algorithm","volume":"Volume 12117","author":"Silva","year":"2020","journal-title":"Proceedings of the Foundations of Intelligent Systems. 25th International Symposium, ISMIS 2020"},{"key":"ref_16","first-page":"293","article-title":"CSPLib: A benchmark library for constraints","volume":"5","author":"Gent","year":"2001","journal-title":"Constraints"},{"key":"ref_17","first-page":"195","article-title":"Benchmarking and fairness in constraint satisfaction solving","volume":"27","author":"Liffiton","year":"2022","journal-title":"Constraints"},{"key":"ref_18","first-page":"1","article-title":"Improving reproducibility in machine learning research (a report from the NeurIPS 2019 reproducibility program)","volume":"22","author":"Pineau","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vidal-Silva, C., Duarte, V., Cardenas-Cobo, J., Serrano-Malebran, J., Veas, I., and Rubio-Le\u00f3n, J. (2023). Reviewing Automated Analysis of Feature Model Solutions for the Product Configuration. Appl. Sci., 13.","DOI":"10.3390\/app13010174"},{"key":"ref_20","unstructured":"Pohl, K., Heymans, P., Kang, K.C., and Metzger, A. (2007, January 16\u201318). FAMA: Tooling a Framework for the Automated Analysis of Feature Models. Proceedings of the First International Workshop on Variability Modelling of Software-Intensive Systems (VaMoS 2007), Limerick, Ireland. Lero Technical Report."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/978-3-642-38977-1_11","article-title":"Automated Analysis in Feature Modelling and Product Configuration","volume":"Volume 7925","author":"Favaro","year":"2013","journal-title":"Proceedings of the Safe and Secure Software Reuse\u2014ICSR 2013"},{"key":"ref_22","first-page":"76","article-title":"Enhancing reproducibility in computational methods","volume":"2","author":"Stodden","year":"2022","journal-title":"Nat. Comput. Sci."},{"key":"ref_23","first-page":"415","article-title":"cvc5: A Versatile and Industrial-Strength SMT Solver","volume":"Volume 13243","author":"Fisman","year":"2022","journal-title":"Proceedings of the Tools and Algorithms for the Construction and Analysis of Systems\u2014TACAS 2022"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Penco, R., Pintar, D., Vrani\u0107, M., and \u0160o\u0161tari\u0107, M. (2025). Large Language Model-Driven Framework for Automated Constraint Model Generation in Configuration Problems. Appl. Sci., 15.","DOI":"10.3390\/app15126518"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Usman, M., Wang, W., Vasic, M., Wang, K., Vikalo, H., and Khurshid, S. (2020, January 15\u201320). A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML). Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020), London, UK.","DOI":"10.1145\/3395647"},{"key":"ref_26","first-page":"255","article-title":"Hybrid Symbolic-Neural Reasoning for Large-Scale Constraint Solving","volume":"76","author":"Xu","year":"2023","journal-title":"J. Artif. Intell. Res."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/9\/139\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:35:20Z","timestamp":1760034920000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/9\/139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":26,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["data10090139"],"URL":"https:\/\/doi.org\/10.3390\/data10090139","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,29]]}}}