Cascading aggregations work by specifying a set of key metrics, a set of thresholds for those and a set of functions that can extract interesting pieces of information or combine two other functions. To react efficiently on current events, aggregation functions always work on data streams. Insights can be generated by linking metric threshold violations to aggregation functions; this creates a graph of aggregations, which, when topologically shorted, can lead to generation of summarized information.

What we want to reach to is automated data summaries that read like the one below.

Version 1.2.1 (commit a223b) of app Foo is receiving negative feedback (sentiment ratio: 0.45%) on app store. Users are complaining about frequent crashes.

Top exceptions in app crash log: NullPointerException (88%), increased 95% in version 1.2.1.

Static analysis on commit a223b indicates possible uninitialised variable x in, line 75.

Commit a223b is 85% bigger than average. Code review passed with no feedback.

What we need is an API that allows to build a high-level trickle of meaningful alerts out of a torrent of low-level signal streams, and show how it scales.