论文标题
一种有效的启发式方法，结合了最大项和面积测量，以压缩大量的表约束
An efficient heuristic approach combining maximal itemsets and area measure for compressing voluminous table constraints
论文作者
论文摘要
约束编程是建模和解决组合问题的强大范式。尽管有许多限制，但表约束也许是最重要的研究最重要的，并且能够编码在有限变量上定义的任何其他约束。但是，限制可能非常庞大，它们的大小可以随着其敏锐而成倍增长。为了减少空间和时间复杂性，研究人员专注于各种形式的压缩。在本文中，我们提出了一种基于最大频繁项目集技术和面积测量的新方法，以列举与压缩表约束相关的最大频繁项目集。我们的实验结果表明，这种方法在压缩和解决压缩表约束方面的有效性和效率。
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.
