Abstract
It is well established that missing values, if not dealt with properly, may lead to poor data analytics models, misleading conclusions, and limitation in the generalization of findings. A key challenge in detecting these missing values is when they manifest themselves in a form that is otherwise valid, making it hard to distinguish them from other legitimate values. We propose to demonstrate FAHES, a system for detecting different types of disguised missing values (DMVs) which often occur in real world data. FAHES consists of several components, namely a profiler to generate rules for detecting repeated patterns, an outlier detection module, and a module to detect values that are used repeatedly in random records. Using several real world datasets, we will demonstrate how FAHES can easily catch DMVs.