Trace Mining from Distributed Assembly Databases for Causal Analysis

Abstract

Hierarchical structures of components often appear in industry, such as the components of cars. We focus on association mining from the hierarchically assembled data items that are characterized with identity labels such as lot numbers. Massive and physically distributed product databases make it difficult to directly find the associations of deep-level items. We propose a top-down algorithm using virtual lot numbers to mine association rules from the hierarchical databases. Virtual lot numbers delegate the identity information of the subcomponents to upper-level lot numbers without modifications to the databases. Our pruning method reduces the number of enumerated items and avoids redundant access to the databases. Experiments show that the algorithm works an order of magnitude faster than a naive approach.