  Data cubes are multidimensional databases, often built from several separate
databases, that serve as flexible basis for data analysis. Surprisingly,
outlier detection on data cubes has not yet been treated extensively. In this
work, we provide the first framework to evaluate robust outlier detection
methods in data cubes (RODD). We introduce a novel random forest-based outlier
detection approach (RODD-RF) and compare it with more traditional methods based
on robust location estimators. We propose a general type of test data and
examine all methods in a simulation study. Moreover, we apply ROOD-RF to real
world data. The results show that RODD-RF can lead to improved outlier
detection.
