Data Annotation (labels)
t6 is focused on timeseries, Data-annotation process is classifying Datapoints from Flows using categories. Annotations and Categories are going to be used in the Exploratory Data Analysis process and in Machine-Learning.
Tagged on #feature, #data-annotation, #label, #labelisation,
Categories
A classification category is a dataset containing a name, a descriptive content and a color.
Users on t6 can customize their own Categories - and there are constraints on the number of Categories per users. There is no process of approval on the annotations.
Categories
are playing a major key for Machine-Learning as they are used in Traniing process.
Annotations / labels
Data-annotation can be associated to Datapoints using any of the following processes:
- Adding manually an annotation with the specific annotation Api endpoint. This hand process is strong but require manual annotation.
- Adding annotations programmatically from a Decision Rule during Datapoint creation.
Each annotation are set for a range of date/time, so the category_id associated to the Annotation can be defined on a specific timespan. Both process are expecting to handle large datasets.
Tagged on #feature, #data-annotation, #label, #labelisation,