There is no shortage of outlier detection (OD) algorithms in the literature, yet a vast body of them are designed for a single machine. With the increasing reality of already cloud-resident datasets comes the need for distributed OD techniques. This area, however, is not only understudied but also short of public-domain implementations for practical use. This paper aims to fill this gap: We design Sparx, a data-parallel OD algorithm suitable for shared-nothing infrastructures, which we specifically implement in Apache Spark. Through extensive experiments on three real-world datasets, with several billions of points and millions of features, we show that existing open-source solutions fail to scale up; either by large number of points or high dimensionality, whereas Sparx yields scalable and effective performance. To facilitate practical use of OD on modern-scale datasets, we open-source Sparx under the Apache license at https://tinyurl.com/sparx2022.

In recent years, it has become clear that the brain maintains a temporal memory of recent events stretching far into the past. This letter presents a neurally inspired algorithm to use a scale-invariant temporal representation of the past to predict a scale-invariant future. The result is a scale-invariant estimate of future events as a function of the time at which they are expected to occur. The algorithm is time-local, with credit assigned to the present event by observing how it affects the prediction of the future. To illustrate the potential utility of this approach, we test the model on simultaneous renewal processes with different timescales. The algorithm scales well on these problems despite the fact that the number of states needed to describe them as a Markov process grows exponentially.