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Analyzing data to find trends, correlations, and stablepatterns is an important problem for many industrialapplications. In this paper, we propose a new techniquebased on parallel coordinates visualization. Previous workon parallel coordinates methods has shown that they areeffective only when variables that are correlated and/orshow similar patterns are displayed adjacently. Althoughcurrent parallel coordinates tools allow the user tomanually rearrange the order of variables, this process isvery time-consuming when the number of variables islarge. Automated assistance is needed. This paperproposes an edit-distance based technique to rearrangevariables so that interesting patterns can be easilydetected. Our system, V-Miner, includes both automatedmethods for visualizing common patterns and a query toolthat enables the user to describe specific target patterns tobe mined/displayed by the system. Following an overviewof the system, a case study is presented to explain howMotorola engineers have used V-Miner to identifysignificant patterns in their product test and design data.