Definition (BAnalyticDataDefinition)

This optional component configures the default values for facets, aggregation and rollup properties. Each tag from the Haystack, Niagara or a custom dictionary requires a definition. An algorithm or a graphics binding (Px view or Ux chart) defines the tag to use for searching a hierarchy and the node at which to begin the search. Using this information the framework retrieves trend data. It then combines the retrieved data using the facets, aggregation and rollup properties defined on the definition that is associated with the tag.

Data Definition components simplify analytic setting up an application by configuring typical properties in one place, which are then applied as defaults throughout the application. When you create a Data Definition for a specific data item, you can still override its properties for a specific analytic request in the Alert, Analytic Proxy Ext or Binding.

Figure 64. Example of a Data Definition properties

To view a definition’s Property Sheet, double-click open the Analytic Data Manager (double-click Config > Services > AnalyticService > Definitions) and click New.

Property	Value	Description
Name	text	Assigns a unique name to each Data Definition component.
Id	`namespace`:`name`	Configures a fully-qualified tag name or tag group name that identifies specific data in the station, such as hs:zoneAirTempSensor to identify Zone Temp sensors. `namespace` is the name of a tag dictionary. `name` is the tag name used to collect point output in a hierarchy. Tagging a Data Definition automatically associates the properties defined by the definition with the tag that shares the same name.
Aggregation	drop-down list (defaults to `First`)	Configures the default function to apply when the analytic request needs to combine values from multiple data sources into a single value. This applies to both value and trend requests. If aggregation is not enabled in the binding/settings window, the aggregation value defined in the Data Definition applies to all chart bindings, reports and tables. `And` returns the logical “and” of Boolean values. `Avg` returns the statistical mean, which is determined by calculating the sum of all values and dividing by the number of values. `Count` returns the total number or quantity of values in a combination. If you request this value on a binding in a PX view, the system counts the number of values based on the properties defined by the data source block and the algorithm’s Property Sheet. `First` returns the first value in the combination. `Last` returns the last value in the combination. `Max` returns the highest value in the combination. `Median` returns the value in the middle of a sorted combination—the number that separates the higher half from the lower half. `Min` returns the lowest value in the combination. `Mode` returns the statistically most frequently occurring number in the combination. `Or` returns the logical “or” of Boolean values. `Range` returns the statistical difference between the largest and smallest values in the combination. `Sum` adds together all values in the combination resulting in a single value. `Load Factor` returns the average value divided by peak value. `Std Dev` returns the standard deviation of the values in the combination.
Rollup	drop-down list (defaults to `First`)	Configures the default function to apply when the analytic request needs to rollup records from a single data source into less granular records. This typically only applies to trend request, but may also apply to value requests where the `Id` is an algorithm that contains a block like Runtime or Sliding Window, which processes a trend request. If rollup is not enabled in the binding/settings window, the rollup value configured in the Data Definition applies to all chart bindings, reports and tables. `And` returns the logical “and” of Boolean values. `Avg` returns the statistical mean, which is determined by calculating the sum of all values and dividing by the number of values. `Count` returns the total number or quantity of values in a combination. If you request this value on a binding in a PX view, the system counts the number of values based on the properties defined by the data source block and the algorithm’s Property Sheet. `First` returns the first value in the combination. `Last` returns the last value in the combination. `Max` returns the highest value in the combination. `Median` returns the value in the middle of a sorted combination—the number that separates the higher half from the lower half. `Min` returns the lowest value in the combination. `Mode` returns the statistically most frequently occurring number in the combination. `Or` returns the logical “or” of Boolean values. `Range` returns the statistical difference between the largest and smallest values in the combination. `Sum` adds together all values in the combination resulting in a single value. `Std Dev` calculates the standard deviation of the values in the combination. `Load Factor` calculates the average divided by peak (Max) value.
Facets	units, precision, min, max, etc.	Clicking the chevron to the right of this property o pens a standard Config Facets window. If no facets are defined, these values default to the default facets configured in the tag associated with the point. The facets you configure here override the default facets.
Missing Data Strategy, Use This Value	check box	Enables and disables missing data interpolation for the current value. When enabled, the framework applies this strategy to all requests. When enabled, the framework applies this strategy to all requests.
Missing Data Strategy, Aggregation Strategy	drop-down list	Configures how the framework handles missing trend data (data in a series) when processing analytic requests and one or more records are missing for an interval. It applies when even a single record for an interval is missing. It does not apply to value requests. NOTE: If the analytic trend request specifies `Interval` = `none`, the framework ignores the Missing Data Strategy. `Ignore Series` ignores the entire series if any record even one interval is missing. `Ignore Point` ignores any missing records and aggregates the values in the existing records.
Missing Data Strategy, Interpolation Algorithm	drop-down list	Defines the algorithm used to interpolate values for missing values (missing records). `None` does not interpolate values for missing records. `Linear Interpolation` replaces a missing record by linearly interpolating the missing value using the prior and next records on either side of the missing record. `K-Nearest Neighbor` is for numeric, enum and Boolean records. This strategy replaces a missing value by calculating the majority value recorded for the item’s nearest K number of neighbors.
Missing Data Strategy, K Value	Numeric field editor (default = 1, min =1, max = 30)	Defines the number of records used by the configured interpolation algorithm when a record is missing to calculate the interpolated value.
Outlier, Status	Status check boxes (disabled, fault, down, stale, and null are checked by default)	Configures filtering behavior to remove records from a dataset based on the status flags or value of each record. Status values include: disabled, fault, down, alarm, stale, overridden, null, unackedAlarm and NaN (Not a Number. This is another way, similar to the InvalidValueFilter block, to filter records based on bad status conditions. 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. You may configure additional properties for High Limit and Low Limit (defaults to null check box selected, which does not enforce a limit). This filtering does not apply to value requests. Algorithm blocks may perform additional filtering based on statuses or values. After the framework filters out the records with invalid data, use a missing data strategy to interpolate valid data.
Outlier, RawDataFilter, High Limit	null check box (defaults to checked) or numeric value	Optionally, defines a number above which a data value (an outlier) should be excluded from an analytic calculation. Setting a limit is similar to using a RangeFilter block in an algorithm where values greater than the high limit are excluded from being processed and using an InvalidValueFilter to filter NaN numbers without the benefit of also filtering infinite values. A use case might be that you have a sensor with a range of 0-150 deg F, any readings above 150 would be anomalies or suspect values, which need to filtered out.
Outlier, RawDataFilter, Low Limit	null check box (defaults to checked) or numeric value	Optionally, defines a number below which a data value (an outlier) should be excluded from an analytic calculation. Setting a limit is similar to using a RangeFilter block in an algorithm where values lower than the low limit are excluded from being processed and using an InvalidValueFilter to filter NaN numbers without the benefit of also filtering infinite values. A use case might be that you have a sensor with a range of 0-150 deg F, any readings below zero would be anomalies or suspect values, which need to filtered out.
Outlier, Delta Values, High Limit	null check box (defaults to checked) or numeric value	Sets a high limit that applies when Analytics is calculating a delta value. You might have a history for electrical energy consumption (KWH) that is totalized, which means that an ever increasing value is being logged. Analytics gets the delta values (difference between each record) to show the electrical consumption for a period like 15 minutes or a day. The `High Limit` property would filter out a calculated delta value that exceeds the configured high limit.
Outlier, Delta Values, Low Limit	null check box (defaults to checked) or numeric value	Sets a low limit that applies when Analytics is calculating a delta value. You might have a history for electrical energy consumption (KWH) that is totalized, which means that an ever increasing value is being logged. Analytics gets the delta values (difference between each record) to show the electrical consumption for a period like 15 minutes or a day. The `Low Limit` property would filter out a calculated delta value that is lower than the configured low limit.
outlier		These properties are duplicate properties of the Outlier Handling properties above. You do not need to configure them.
RawDataFilter, Values
RawDataFilter, High Limit
RawDataFilter, Low Limit

Definition (BAnalyticDataDefinition)

Figure 64. Example of a Data Definition properties

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