A dataview is a user selected subset of data from one or more streams presented in tabular
format. Α data view is created by defining the target namespace, the source stream of streams,
the desired data fields, time period and interpolation interval. Τhe ability to
create data views in OCS matches directly with OSIsoft's data science enablement efforts,
were by users will be able to programmatically access data view
content for the purposes of data science. In this section we will create a new data
view for the wind turbine data so that it is accessible externally in a jupyter notebook.
Once the relevant stream metadata has been added we now need to prepare our wind farm dataset to
be used for machine learning with the relevant associated metadata. For this purpose we utilize
the data view feature in OCS under the analytics section of the navigation menu. To create a new
data view click the add data view button in the data views pane. In the query value bar
enter GE star. This will return all data streams pertaining to the ten wind turbines. Click apply.
On the next screen under index configuration keep the default settings for start index,
end index and time interval. These settings are not important now as they will be changed
programmatically later. If the save defaults with data view box is checked the configuration set in
these fields will be maintained the next time the data view is accessed via the user interface. When
the apply button is clicked with the default field configuration the resulting data view is such.
We need to modify the fields displayed so that the data set is more relevant for our application.
Since we added the turbine and measurement metadata fields,
we can see these fields under the field management section.
Uncheck all fields
except for timestamp, turbine, measurement and value. Bring the checked fields up.
The fields can be moved up or down by clicking on the up or down arrow buttons.
Clicking the apply button again will update the generated data view.
We now see that the turbine and measurement columns are repeated several times with
integer suffixes on the column names. With the measurement column showing the metadata key value,
blade one actual value instead of the measurement's numerical value. Thus we
need to group these measurements and values by a specific turbine, identify the value filled
by the measurement key value and not display the measurement value as rows in the table.
Thus uncheck the measurement field ; lick on the turbine field and select
the group by button. This groups the data streams by the turbine metadata.
Then click on the measurement field and select the identify by button; this identifies the data
streams by the measurement metadata. Click the apply button to refresh the resulting data view.
The data view now appears to be in the correct format needed. Enter the name for the data view
in this format, where these are your initials and this is today's date in four-digit year,
two-digit month and two-digit day respectively. Click on the Save button.
We have successfully created our data view.
In the next section we will configure an OCS client in order to access this
data view externally in a jupyter notebook.