Filtering data with the status filter

This filter identifies records with invalid data. After removing the records, you can configure the framework to interpolate the missing data using a missing data strategy. Follow these steps before applying a missing data strategy.

Prerequisites: You are using Workbench in a Supervisor or controller station. The database contains history records collected from tagged devices for which the data definition exists.

Navigate to Config > Services > AnalyticService and double-click the data definition associated with the tag and device.
The edit view of the data definition opens.
Outlier Status options default to disabled, fault, down, stale, and null. This means that if you make no changes, the framework removes from the data set history those records whose Status value is one of these.
To filter (remove) records that have specific statuses, select one or more check boxes under Outlier.
To retain records that the framework would otherwise remove, click to remove one or more check marks.
To continue, click Save.
If you check all boxes, the framework filters out all records except those with a status of {ok}, which is always enabled. If you check no box, the framework filters out no records based on Status.

The framework removes all records with the selected Status values from the data set.

For example, here is a set of historical data:

Timestamp	Data value	Status
12/15/21 11:00	25	{fault}
12/15/21 12:00	25	{null}
12/15/21 13:00	45	{ok}
12/15/21 14:00	56	{overridden, alarm}

If you select the fault and null check boxes for Outlier, only these records pass through:

Timestamp	Data value	Status
12/15/21 13:00	45	{ok}
12/15/21 14:00	56	{overridden, alarm}

You would now interpolate the missing data using linear interpolation or K nearest neighbor. Following interpolation, the data would look like this:

Timestamp	Data value	Status	Trend Flags
12/15/21 11:00	45	{ok}	{li}
12/15/21 12:00	45	{ok}	{li}
12/15/21 13:00	45	{ok}	{}
12/15/21 14:00	56	{overridden, alarm}	{}

If there is no preceding record, the linear interpolation uses the first available record. If there is a valid preceding and post record, linear interpolation calculates a value between to two values. If the 10:00 value was 30, the interpolated values would be 11:00 35 and 12:00 40.

An algorithm can process these data directly to create a graph or other visual representation of the data.

Filtering data with the status filter

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