Universal Data Discovery Using Atypicality

With the enormous amount of data generated through the internet and sensors, Internet of Things, it becomes too overwhelming for humans to examine it all. One solution is to reduce the data to a set of statistics. The perspective in this paper is the opposite, namely that most of this data is just background noise, and the interesting parts are those that deviate from background noise, the parts that are atypical.In order to find such "interesting" parts of data, universal approaches are required, since it is not known in advance what we are looking for. The paper develops new algorithms for detecting atypical data based on information theory concepts. These are applied to a number of real-world data.