FASTDIAGP: An Algorithm for Parallelized Direct Diagnosis

Constraint-based applications attempt to identify a solution that meets all defined user requirements. If the requirements are inconsistent with the underlying constraint set, algorithms that compute diagnoses for inconsistent constraints should be implemented to help users resolve the “no solution could be found” dilemma. FastDiag is a typical direct diagnosis algorithm that supports diagnosis calculation without pre-determining conflicts. However, this approach faces runtime performance issues, especially when analyzing complex and large-scale knowledge bases. In this paper, we propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming. This algorithm extends FastDiag by integrating a parallelization mechanism that anticipates and pre-calculates consistency checks requested by FastDiag. This mechanism helps to provide consistency checks with fast answers and boosts the algorithm’s runtime performance. The performance improvements of our proposed algorithm have been shown through empirical results using the Linux-2.6.3.33 configuration knowledge base.

Authors

• Viet-Man Le Graz University of Technology, Graz, Austria University of Economics, Hue University, Hue, Vietnam
• Cristian Vidal Silva Universidad de Talca, Talca, Chile
• Alexander Felfernig Graz University of Technology, Graz, Austria
• David Benavides University of Sevilla, Seville, Spain
• José Galindo University of Sevilla, Seville, Spain
• Thi Ngoc Trang Tran Graz University of Technology, Graz, Austria School of Hospitality and Tourism, Hue University, Hue, Vietnam

Keywords

• CSO: Applications
• KRR: Applications
• KRR: Diagnosis and Abductive Reasoning
• KRR: Knowledge Engineering