document: Distributed data mining in a chain store database of short transactions In this paper , we broaden the horizon of traditional rule mining by introducing a new framework of causality rule mining in a distributed chain store database . Specifically , the causality rule explored in this paper consists of a sequence of triggering events and a set of consequential events , and is designed with the capability of mining non-sequential , inter-transaction information . Hence , the causality rule mining provides a very general framework for rule derivation . Note , however , that the procedure of causality rule mining is very costly particularly in the presence of a huge number of candidate sets and a distributed database , and in our opinion , can not be dealt with by direct extensions from existing rule mining methods . Consequently , we devise in this paper a series of level matching algorithms , including Level Matching ( abbreviatedly as LM ) , Level Matching with Selective Scan ( abbreviatedly as LMS ) , and Distributed Level Matching ( abbreviatedly as Distibuted LM ) , to minimize the computing cost needed for the distributed data mining of causality rules . In addition , the phenomena of time window constraints are also taken into consideration for the development of our algorithms . As a result of properly employing the technologies of level matching and selective scan , the proposed algorithms present good efficiency and scalability in the mining of local and global causality rules . Scale-up experiments show that the proposed algorithms scale well with the number of sites and the number of customer transactions . Index Terms : knowledge discovery , distributed data mining causality rules , triggering events , consequential events
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