In this article I tackle the following problem: how to define and distinguish anomalies (spikes, peaks, and outliers in data) in real-life, production situations. Typically, the data drift results in the absence of a reference level. Since we do not
I am looking at distribution of a certain data set (left). It has two peaks (this is called ‘bimodal’) therefore I suspect that those are two overimposed populations. How do I split the data, to rediscover the original two populations
Here is how one careless sentence triggered a surge of detergents in our oceans.
I recently work a lot with IT Infrastructure Management data. At Sopra Steria, we manage sizeable ecosystems of our corporate clients that include thousands of apps and infrastructure elements. We handle events, incidents, alarms, and support tickets. We process thousands