Range Analysis and Applications to Root Causing We propose a supervised learning algorithm whose aim is to derive features that explain the response variable better than the original features. Moreover, when there is a meaning for positive vs negative samples, our aim is to derive features that explain the positive samples, or subsets of positive samples that have the same rootcause. Each derived feature represents a single or multidimensional subspace of the feature space, where each dimension is specified as a featurerange pair for numeric features, and as a featurelevel pair for categorical features. Unlike most Rule Learning and Subgroup Discovery algorithms, the response variable can be numeric, and our algorithm does not require a discretization of the response. The algorithm has been applied successfully to numerous reallife rootcausing tasks in chip design, manufacturing, and validation, at Intel.

Generic and Robust Localization of Multidimensional Root Causes Operators of online software services periodically collect various measures with many attributes. When a measure becomes abnormal, indicating service problems such as reliability degrade, operators would like to rapidly and accurately localize the root cause attribute combinations within a huge multidimensional search space. Unfortunately, previous approaches are not generic or robust in that they all suffer from impractical root cause assumptions, handling only directly collected measures but not derived ones, handling only anomalies with signicant magnitudes but not those insignicant but important ones, requiring manual parameter netuning, or being too slow. This paper proposes a generic and robust multidimensional root cause localization approach, Squeeze, that overcomes all above limitations, the first in the literature. Through our novel bottomup then topdown searching strategy and the techniques based on our proposed generalized ripple effect and generalized potential score, Squeeze is able to reach a good trade off between search speed and accuracy in a generic and robust manner. Case studies in several banks and an Internet company show that Squeeze can localize root causes much more rapidly and accurately than the traditional manual analysis. Furthermore, our extensive experiments on semisynthetic datasets show that the F1score of Squeeze outperforms previous approaches by 0.4 on average, while its localization time is only about 10 seconds