Analyzing data with entity-centric analysis

The feature of Elastic’s machine learning entity-centric analytics allows you to analyze your data by utilizing algorithms for classification, outlier detection, and regression. It also enables you to generate new indices that include the results alongside your original data.

If you possess a license that includes machine learning features, you can create jobs for entity-centric analytics and view the outcomes on the Data Frame Analytics page in Kibana. The key features that help with this type of analysis are transforms and DataFrame analytics.

Let’s understand both.

Transforms

Transforms are specific implementations that are used to convert typical time series data into entity-centric data so that we can categorize the data into specific entities. We can do this by creating new indices with summarized data in them. Transforms work by helping us leverage their continuous mode functionality, where we can not only...