Tags: direct and implied

Tagging identifies, in a consistent way, the data to include in an analytic request. This may be the most important set up task and is the last step before running a query.

There are two ways to tag points so that the framework can find the data to analyze:

with a direct tag
with an implied tag

Setting up points with direct tags can be a very time-consuming process, especially if you have hundreds of points to analyze.

Using implied tags is the best practice. Implied tags rely on rules you set up to assign tags to points. These rules depend on your point naming convention. The best way to tag data is with a tag rule.

Three standard (default) dictionaries are available with the framework.

The Niagara dictionary contains commonly-used tags.
The Haystack dictionary is an open source dictionary created by Project Haystack to “streamline working with data from the Internet of Things.” (project-haystack.org).
The Niagara Analytics Framework comes with a third pre-configured Analytics Tag Dictionary.

The framework automatically created an Analytics tag dictionary for you when you added the AnalyticService to the station. A pre-configured Analytics dictionary in the analytics-lib palette includes the additional tag and tag group definitions used with the provided algorithms. Before you create your own dictionary, view the tags provided by these tag dictionaries. They may meet your needs.

Tags: direct and implied

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