Explaining outliers is a topic that attracts a lot of interest; however existing proposals focus on the identification of the relevant dimensions. We extend this rationale for unsupervised distance-based outlier detection, and through investigating subspaces, we propose a novel labeling of outliers in a manner that is intuitive for the user and does not require any training at runtime. Moreover, our solution is applicable to online settings and a complete prototype for detecting and explaining outliers in data streams using massive parallelism has been implemented. Our solution is evaluated in terms of both the quality of the labels derived and the performance.

In this work, we focus on distance-based outliers in a metric space, where the status of an entity as to whether it is an outlier is based on the number of other entities in its neighborhood. In the recent years, several solutions have tackled the problem of distance-based outliers in data streams, where outliers must be mined continuously as new elements become available. An interesting research problem is to combine the streaming environment with massively parallel systems to provide scalable stream-based algorithms. However, none of the previously proposed techniques refer to a massively parallel setting. Our proposal fills this gap and studies transferring state-of-the-art techniques in Apache Flink, a modern platform for intensive streaming analytics. We thoroughly present the technical challenges encountered and the alternatives that may be applied. We show speed-ups up to 117 (resp. 2076) times over a naive parallel (resp. non-parallel) solution in Flink, by using just an ordinary 4-core machine and a real-world dataset. Our results demonstrate that oulier mining can be achieved in an efficient and scalable manner. The resulting techniques have been made publicly available in open-source.