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Exploiting Data Mining Techniques for Compressing Table Constraints

Soufia Bennai Kamal Amroun Samir Loudni 1 
1 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : In this paper, we propose an improvement of the compression step of sliced table method proposed by Gharbi et al. [1] for compressing and solving table constraints. We consider only n-ary CSP defined in extensional form. More precisely, we propose to use the cover of an itemset in the FP-tree instead of its frequency to improve the construction step of the resulting compressed tables. Moreover, we propose to exploit the compression rate metric instead of savings to compute frequent itemsets relevant for compression. This allows higher compression and leads to an efficient resolution of compressed tables by identifying more accurate frequent itemsets necessary for compression. Experimental results show the effectiveness and efficiency of our approach.
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Soufia Bennai, Kamal Amroun, Samir Loudni. Exploiting Data Mining Techniques for Compressing Table Constraints. IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2019, Portland, Oregon, United States. ⟨10.1109/ICTAI.2019.00015⟩. ⟨hal-02463468⟩



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