Sliced Table Constraints: Combining Compression and Tabular Reduction

Nebras Gharbi (1, 2) , Fred Hemery (1) , Christophe Lecoutre (1) , Olivier Roussel (1)

1 CRIL - Centre de Recherche en Informatique de Lens
2 INSERM - Institut National de la Santé et de la Recherche Médicale

Many industrial applications require the use of table constraints (e.g., in configuration problems), sometimes of significant size. During the recent years, researchers have focused on reducing space and time complexities of this type of constraint. Static and dynamic reduction based approaches have been proposed giving new compact representations of table constraints and effective filtering algorithms. In this paper, we study the possibility of combining both static and dynamic reduction techniques by proposing a new compressed form of table constraints based on frequent pattern detection, and exploiting it in STR (Simple Tabular Reduction).

Domaines: Intelligence artificielle [cs.AI]

Nebras Gharbi, Fred Hemery, Christophe Lecoutre, Olivier Roussel. Sliced Table Constraints: Combining Compression and Tabular Reduction. Integration of AI and OR Techniques in Constraint Programming (CPAIOR 2014), May 2014, Cork, Ireland. pp.120-135, ⟨10.1007/978-3-319-07046-9_9⟩. ⟨hal-01141409⟩