An efficient heuristic approach combining maximal itemsets and area measure for compressing voluminous table constraints
This addresses space and time complexity issues for researchers and practitioners in Constraint Programming, but appears incremental as it builds on existing compression techniques.
The paper tackles the problem of compressing voluminous table constraints in Constraint Programming by proposing a new approach based on maximal frequent itemsets and area measure, showing effectiveness and efficiency in compression and solving compressed constraints.
Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint is perhaps the most significant-being the most well-studied and has the ability to encode any other constraints defined on finite variables. However, constraints can be very voluminous and their size can grow exponentially with their arity. To reduce space and the time complexity, researchers have focused on various forms of compression. In this paper we propose a new approach based on maximal frequent itemsets technique and area measure for enumerating the maximal frequent itemsets relevant for compressing table constraints. Our experimental results show the effectiveness and efficiency of this approach on compression and on solving compressed table constraints.