abcOD: Mining Band Order Dependencies

Abstract

We present the design of and a demonstration plan for abcOD, a tool for efficiently discovering approximate band conditional order dependencies (abcODs) from data. abcOD utilizes a dynamic programming algorithm based on a longest monotonic band. Using real datasets, we demonstrate how the discovered abcODs can help users understand ordered data semantics, identify potential data quality problems, and interactively clean the data