Working with Patterns

7 Working with Patterns

Patterns are much simpler than standard explorations. When working from a pattern, you need to specify a few key fields to discover an interesting result. Once you have created a pattern, it will appear in the Catalog just like any other exploration. You can use the patterns to create new explorations.

A pattern is a template of an Oracle Stream Analytics application that already has the business logic built into it. The visual representation of the event stream varies from one pattern type to another based on the key fields you choose. A pattern provides you with a simple way to explore event Streams based on common business scenarios. Use the filters at left to view different categories of pattern and then click on Use this pattern to create an exploration. You can see full descriptions and learn more about each pattern by clicking the box that contains it. Click again to hide the extra information.

A pattern provides you with the results displayed in a live output stream based on common business scenarios.

Note:

While entering data in the fields for a specific pattern, ensure that the data you enter corresponds to the datatype of the field. If there is a mismatch between the entered data and the datatype, the pattern yields incorrect results.

You can include or exclude patterns based on their categories using the View All link in the left panel under Show Me. When you click View All, a tick mark appears beside it and all the patterns are displayed in the page.

When you want to display/view only a few/selective patterns, deselect View All and select the individual patterns. Only the selected patterns will be shown in the catalog.

Figure 7-1 Select Patterns

Description of "Figure 7-1 Select Patterns"

The following table categorizes the patterns.

Table 7-1 Categories of Patterns

Category	Pattern	New/Existing
General	Change Detector	New
	‘A’ Followed by ‘B’	New
	‘A’ Not Followed by ‘B’	New
	Bottom N	Existing
	Detect Duplicates	Existing
	Down Trend	Existing
	Eliminate Duplicates	Existing
	Fluctuation	Existing
	Inverse W	Existing
	Detect Missing Heartbeat	New
	Top N	Existing
	Up Trend	Existing
	W	Existing
	Union	New
	Left Outer Join	New
Machine Learning	K-Means. Anomaly Detection	New
Spatial	Spatial General	New
Statistical	Median	New
	Correlation	New
	Quantile	New
	Standard Deviation	New

7.1 Understanding Patterns

Oracle Stream Analytics provides a feature known as patterns. A pattern provides you with the results displayed in a live output stream based on common business scenarios.

7.2 Creating a Pattern

Oracle Stream Analytics provides various patterns.

The various types or categories of patterns available are:

General
Machine Learning
Spatial
Statistical.

The entire list of patterns is displayed category-wise on this dialog. Hover over each of the patterns to see its description.

To create a pattern:

Navigate to the Catalog.
Click Create New Item > Pattern.
The Select a Pattern to Create dialog opens.

Figure 7-2 Select a Pattern to Create Dialog

Description of "Figure 7-2 Select a Pattern to Create Dialog"
Click the pattern that you want to create.

Figure 7-3 Create a <pattern> Exploration

Description of "Figure 7-3 Create a <pattern> Exploration"
Enter a Name for the exploration. This is a mandatory field.
Enter a Description for the exploration. This is an optional field.
Enter suitable Tags for the exploration. This is an optional field.
Click Create. An editor to create a pattern with the exploration you just created appears.

Figure 7-4 Pattern Editor

Description of "Figure 7-4 Pattern Editor"

The selected pattern is created.

An alternate way to create a pattern is to Click Patterns and select Use this pattern on the required pattern tile.

Figure 7-5 Use this Pattern

Description of "Figure 7-5 Use this Pattern"

7.3 Creating Top N Pattern

Use this pattern to obtain the first N events in a window range.

To create a Top N pattern:

Navigate to the Catalog.
Select Create New Item >Pattern > Top N. You can also click Patterns and select Top N.The create pattern screen appears as shown below:

Figure 7-6 Create Top N Pattern

Description of "Figure 7-6 Create Top N Pattern"
Select an Event Stream.
Enter a value for Window Range and select its unit (one of nanoseconds, milliseconds, microseconds, seconds, minutes, and hours). This number must be greater than or equal to 1.

A Window range is the range from which the output is generated.
Enter a value for Window Slide and select its unit (one of nanoseconds, milliseconds, microseconds, seconds, minutes, and hours). This is the frequency at which you want to refresh the data.
Select a value in the Order by Criteria field. You can select multiple fields.
Enter a value in the Number of Events field. This number must be greater than or equal to 1. This is the number that indicates the number of events to be considered for the pattern.

The pattern is visually represented based on the data you have entered/selected.

7.4 Creating Bottom N Pattern

Use the Bottom N pattern to obtain the last N events in a window range.

This section explains how to create a Bottom N pattern.

To create a Bottom N pattern:

Navigate to the Catalog.
Select Create New Item >Pattern > Bottom N. You can also click Patterns and select Bottom N.The create pattern screen appears as shown below:

Figure 7-7 Create Bottom N Pattern

Description of "Figure 7-7 Create Bottom N Pattern"
Select an Event Stream.
Enter a value for Window Range and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This number must be greater than or equal to 1.

A Window range is the range from which the output is generated.
Enter a value for Window Slide and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This is the frequency at which you want to refresh the data.
Select a value in the Order by Criteria field. You can select multiple fields.
Enter a value in the Number of Events field. This number must be greater than or equal to 1. This is the number that indicates the number of events to be considered for the pattern.

The pattern is visually represented based on the data you have entered/selected.

7.5 Creating Up Trend Pattern

Use this pattern to detect when a numeric event field shows a specified trend change higher in value. For example, use this pattern to identify when the temperature value from a sensor device starts continuously increasing.

To create an Up Trend pattern:

Navigate to the Catalog.
Select Create New Item >Pattern > Up Trend. You can also click Patterns and select Up Trend.The create pattern screen appears as shown below:

Figure 7-8 Create Up Trend Pattern

Description of "Figure 7-8 Create Up Trend Pattern"
Select an Event Stream.
Enter a value for Partition Criteria. This value is used for partitioning the data in the event stream.

These are the fields in which you want to track the trend.
Enter a value for Duration and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This is the interval within which the application looks for a pattern match.
Select a value in the Tracking Field drop-down. You can select only 1 item.

This is a source field whose behavior follows the trend for the pattern to emit the event.

The pattern is visually represented based on the data you have entered/selected.

7.6 Creating Down Trend Pattern

Use this pattern to detect when a numeric event field shows a specified trend change lower in value. For example, use this pattern to identify when the temperature value from a sensor device starts continuously decreasing.

To create a Down Trend pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Down Trend. You can also click Patterns and select Down Trend. The create pattern screen appears as shown below:

Figure 7-9 Create Down Trend Pattern

Description of "Figure 7-9 Create Down Trend Pattern"
Select an Event Stream.
Enter a value for Partition Criteria. This field can be used to partition the data in the event stream.

These are the fields in which you want to track the trend.
Enter a value for Duration and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This is the interval within which the application looks for a pattern match.
Select a value in the Tracking Value field. You can select only 1 item.

This is a source field whose behavior follows the trend for the pattern to emit the event.

The pattern is visually represented based on the data you have entered/selected.

7.7 Creating Fluctuation Pattern

Use this pattern to detect when an event data field value changes in a specific upward or downward fashion within a specific time window. For example, use this pattern to identify the variable changes in an Oil Pressure value are maintained within acceptable ranges.

To create a Fluctuation pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Fluctuation. You can also click Patterns and select Fluctuation. The create pattern screen appears as shown below:

Figure 7-10 Create Fluctuation Pattern

Description of "Figure 7-10 Create Fluctuation Pattern"
Select an Event Stream.
Enter a value for Partition Criteria. This field can be used to partition the data in the event stream.

These are the fields in which you want to track the trend.
Enter a value in the Tracking Field. This value is used to track the event data and create a pattern in the live output stream.
Enter a value for Window and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This is the frequency at which you want to refresh the data.
Enter a value for Deviation Threshold %. This value indicates the percentage of deviation you want to be included in the pattern.

This is the interval in which the application looks for a matching pattern.

The pattern is visually represented based on the data you have entered/selected.

7.8 Creating Eliminate Duplicates Pattern

Use this pattern to build an exploration that eliminates duplicate events in your event stream.

To create an Eliminate Duplicates pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Eliminate Duplicates. You can also click Patterns and select Eliminate Duplicates. The create pattern screen appears as shown below:

Figure 7-11 Create Eliminate Duplicates Pattern

Description of "Figure 7-11 Create Eliminate Duplicates Pattern"
Select an Event Stream.
Enter a value for Duplicate Criteria.

This is the field that you want to eliminate for duplicate keys.
Enter a value for Window and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This is the range within which the application searches for duplicates.

The pattern is visually represented based on the data you have entered/selected.

7.9 Creating Detect Duplicates Pattern

Use this pattern to detect when an event data field has duplicate values within a specified period of time. For example, use this pattern when the same order is placed twice within a day.

To create a Detect Duplicates pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Detect Duplicates. You can also click Patterns and select Detect Duplicates. The create pattern screen appears as shown below:

Figure 7-12 Create Detect Duplicates Pattern

Description of "Figure 7-12 Create Detect Duplicates Pattern"
Select an Event Stream.
Enter a value for Duplicate Criteria.

This is the field that you want to look for duplicate keys.
Enter a value for Window and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This is the interval in which the application looks for duplicates.

The pattern is visually represented based on the data you have entered/selected.

7.10 Creating W Pattern

Use this pattern to detect when an event data field value rises and falls in “W” fashion over a specified time window. For example, use this pattern when monitoring a market data feed stock price movement to determine a buy/sell/hold evaluation.

To create a W pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > W. You can also click Patterns and select W. The create pattern screen appears as shown below:

Figure 7-13 Create W Pattern

Description of "Figure 7-13 Create W Pattern"
Select an Event Stream.
Enter a value for Partition Criteria. You can select multiple fields.

These are the fields by which the data is partitioned to form a W.
Enter a value for Window and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This is the interval in which the application looks for a matching pattern.
Select a numeric value for Tracking Value based on which variation constitutes a W-shape for the pattern-based exploration to produce event.

The pattern is visually represented based on the data you have entered/selected.

7.11 Creating Inverse W Pattern

Use this pattern to detect an inverse W pattern with live output stream.

To create an Inverse W pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Inverse W. You can also click Patterns and select Inverse W. The create pattern screen appears as shown below:

Figure 7-14 Create Inverse W Pattern

Description of "Figure 7-14 Create Inverse W Pattern"
Select an Event stream.
Enter a value for Partition Criteria.

This is the field by which the data is partitioned to form an inverse W.
Enter a value for Window and select its unit (one of nanoseconds, milliseconds, seconds, minutes, and hours). This is the interval in which the application looks for a matching pattern.
Select a numeric value for Tracking Value based on which variation constitutes an inverse W-shape for the pattern-based exploration to produce event.

The pattern is visually represented based on the data you have entered/selected.

7.12 Creating 'A' Not Followed by 'B' Pattern

Use this pattern to pattern to detect when 'B' doesn't occur within a specified period of time after 'A'.

To create a ‘A’ Not Followed by ‘B’ pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > ‘A’ Not Followed by ‘B’. You can also click Patterns and select ‘A’ Not Followed by ‘B’. The create pattern screen appears as shown below:

Figure 7-15 Create ‘A’ Not Followed by ‘B’

Description of "Figure 7-15 Create ‘A’ Not Followed by ‘B’"
Select an Event Stream.
Select a field for Partition Criteria.
These are the fields based on which the pattern is formed to illustrate that A is not followed by B.
Select a field for State A: Field.
Select a field for State A: Value. This is the value for State A: Field.
Select a field for State B: Field.
Select a field for State B: Value. This is the value for State B: Field.
Enter a numerical value for the Duration which is greater than zero. You can use the increment and decrement icons to increase or decrease the values. This field is used to observe the pattern for specified duration.

The pattern is visually represented based on the data you have entered/selected.

7.13 Creating A Followed by B Pattern

Use this pattern to detect when an 'A' event is followed by a 'B' event within a specified period of time. Intermediate events are also allowed.

To create a ‘A’ Followed by ‘B’ pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > ‘A’ Followed by ‘B’. You can also click Patterns and select ‘A’ Followed by ‘B’. The create pattern screen appears as shown below:

Figure 7-16 Create A Followed by B Pattern

Description of "Figure 7-16 Create A Followed by B Pattern"
Select an Event Stream.
Select a field for Partition Criteria.
These are the fields based on which the pattern is formed to illustrate that A is followed by B.
Select a field for State A: Field.
Select a field for State A: Value. This is the value for State A: Field.
Select a field for State B: Field.
Select a field for State B: Value. This is the value for State B: Field.
Enter a numerical value for the Duration which is greater than zero. You can use the increment and decrement icons to increase or decrease the values. This field is used to observe the pattern for specified duration.

The pattern is visually represented based on the data you have entered/selected.

7.14 Creating Standard Deviation Pattern

Use this pattern to calculate the standard deviation of the selected values with the expected values.

To create a Standard Deviation pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Standard Deviation. You can also select Patterns and click Standard Deviation. The create pattern screen appears as shown below:

Figure 7-17 Create Standard Deviation Pattern

Description of "Figure 7-17 Create Standard Deviation Pattern"
Select an Event Stream.
Select a field for the Partition Criteria. This field decide the partition criteria in the standard deviation pattern.
Select an Observable Parameter. The standard deviation is calculated based on this parameter.
Enter a numerical value greater than zero for Window. You can use the increment and decrement icons to increase or decrease the values. Remember to select a suitable time unit for the Window. This value indicates the window within which you are tracking the standard deviation.
Enter a numerical value greater than zero for Slide. You can use the increment and decrement icons to increase or decrease the values. Remember to select a suitable time unit for the Window. This value indicates the value by which you can slide the window for calculating the standard deviation.

The pattern is visually represented based on the data you have entered/selected.

7.15 Creating Correlation Pattern

Use this pattern to calculate a correlation between two observable parameters of the live output stream.

To create a Correlation pattern:

Navigate to the Catalog.
Select Create New Item > Patterns > Correlation. You can also click Patterns and select Correlation. The create pattern screen appears as shown below:

Figure 7-18 Create Correlation Pattern

Description of "Figure 7-18 Create Correlation Pattern"
Select an Event Stream.
Select a field for Partition Criteria. This field is used as the criteria for partition.
Select a parameter for Observable Parameter 1. This is the first parameter to observe.
Select a parameter for Observable Parameter 2. This is the second parameter. These parameters are used to calculate a correlation between them.
Enter a numerical value greater than zero for Window. Use increment or decrement icons to increase or decrease the values. Select a suitable time unit. This is the window duration for which the correlation is calculated.
Enter a numerical value greater than zero to be used for the Slide duration. Use the increment or decrement values to increase or decrease the values.

The pattern is visually represented based on the data you have entered/selected.

7.16 Creating Quantile Pattern

Use this pattern to calculate the quantile of the event stream.

To create a Quantile pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Quantile. You can also click Patterns and select Quantile. The create pattern screen appears as shown below:

Figure 7-19 Create Quantile Pattern

Description of "Figure 7-19 Create Quantile Pattern"
Select an Event Stream.
Select a field for Partition Criteria. This field is used as the criteria for partition.
Select a field as the Observable Parameter 1.
Enter a numerical value greater than zero for the Phi-quantile. This value is used to calculate the quantile of the selected event stream.
Enter a numerical value for the Window which is greater than zero. You can use the increment and decrement icons to increase or decrease the values. Select a suitable time unit. This field determines the window within which the quantile is calculated.
Enter a numerical value greater than zero for the Slide. You can use the increment and decrement icons to increase or decrease the values. Select a suitable time unit. This field determines the slide duration.

The pattern is visually represented based on the data you have entered/selected.

7.17 Creating Median Pattern

Use this pattern to calculate the median of an event stream with respect to a specific parameter.

To create a Median pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Median. You can also click Patterns and select Median. The create pattern screen appears as shown below:

Figure 7-20 Create Median Pattern

Description of "Figure 7-20 Create Median Pattern"
Select an Event Stream.
Select a field for Partition Criteria. This field acts as the criteria for the partition.
Select an Observable Parameter. This is the parameter with respect to which the median of the stream is calculated.
Enter a numerical value greater than zero for Window. You can use the increment or decrement icons to increase or decrease the values. Select a suitable time unit. This field indicates the window within which you want to calculate the median.
Enter a numerical value greater than zero for Slide. You can use the increment or decrement icons to increase or decrease the values. Select a suitable time unit. This field indicates the slide of the pattern.

The pattern is visually represented based on the data you have entered/selected.

7.18 Creating Union Pattern

Use this pattern to create a union of events from two streams. The event shape of both the streams must be the same.

To create a Union pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Union. You can also click Patterns and select Union. The create pattern screen appears as shown below:

Figure 7-21 Create Union Pattern

Description of "Figure 7-21 Create Union Pattern"
Select the First event Stream.
Select the Second event Stream. A union of these event stream is created.

Remember:
The shape must be the same for both the event streams.

The pattern is visually represented based on the data you have entered/selected.

7.19 Creating Change Detector Pattern

Use this pattern to detect when one or more parameters are changed within specified period of time.

To create a Change Detector pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Change Detector. You can also click Patterns and select Change Detector. The create pattern screen appears as shown below:

Figure 7-22 Create Change Detector Pattern

Description of "Figure 7-22 Create Change Detector Pattern"
Select an Event Stream.
Select a field for Partition Criteria. This field acts as the criteria for the partition.
Enter a numerical value greater than zero for the Window Range. You can use the increment or decrement icons to increase or decrease the values. Select a suitable time unit.

This field is used to specify the window within which the change needs to be detected.
Select a field for which you to detect the change in Change Criteria (s). You can select multiple fields.
Select Alert on Group Changes if you want to be alerted for all observable parameters are changed together within a specified period of time. Deselect it if you want to be alerted for any one of observable parameters are changed within a specified period of time.

The pattern is visually represented based on the data you have entered/selected.

Oracle Stream Analytics detects changes within the specified period of time and sends output event (alert) either at the end of this period or either at the moment when changes do not happen any more. Let us consider the following graph for example.

Figure 7-23 Change Detector Pattern Example

Description of "Figure 7-23 Change Detector Pattern Example"

Assume you specified duration equal to A-C interval. In "green" case you have values continuously changing from A to B within A-C interval. But, after the point B values do not change any more. So, you will receive alert at the moment B1.

In "blue" case you have values continuously changing during the whole interval - A-C. You will receive alert at the end of specified duration A-C, at the moment C.

Sometimes, you need to get alert immediately after any changes happen. That is in both cases "green" and "blue" cases, you will receive alert at the moment A1 because value had changed and continue receiving alerts until the point B in "green" case and point C in "blue" case.

As a workaround, you can minimize the window value to receive events just after the changes happen.

7.20 Creating K-means.Anomaly Detection Pattern

Use this pattern to explore data clusters and detect anomalies. K-Means is a widely used unsupervised machine learning algorithm for data exploration. K-Means is used in anomaly detection by identifying low density clusters (clusters with very few members), which equates to being less common. Oracle Stream Analytics supports 2 dimensional (2D) space of observations.

To create a K-means.Anomaly Detection pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > K-means.Anomaly Detection. You can also click Patterns and select K-means.Anomaly Detection. The create pattern screen appears as shown below:

Figure 7-24 Create K-means.Anomaly Detection Pattern

Description of "Figure 7-24 Create K-means.Anomaly Detection Pattern"
Select an Event Stream.
Select a Value 1. This value is used to detect anomalies.
Select a Value 2. This value is used to detect anomalies.
Enter a numerical value greater than zero for N-clusters. You can use the increment or decrement icons to increase or decrease the values. This value indicates the number of clusters to be considered for detecting the anomalies.

The pattern is visually represented based on the data you have entered/selected.

7.21 Creating Spatial General Pattern

Use this pattern to analyze streams containing geo-location data and determine how events relate to pre-defined geo-fences in your maps.

To create a Spatial General pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Spatial General. You can also click Patterns and select Spatial General. The create pattern screen appears as shown below:
Figure 7-25 Spatial Pattern

Description of "Figure 7-25 Spatial Pattern"

The color coded on the map denote that:
- Black - indicates that no event is missed
- Orange - indicates that an event has entered the specified range
- Blue - indicates that an event has is near a specified range
- Purple - indicates that an event is within the stay range
- Green - indicates that an event has exited the specified range.
Select a Map. Maps that you have created earlier appear in this list.
Select an Event Stream.
Select a field to be used as the Latitude for the spatial.
Select a field to be used as the Longitude for the spatial.
Select a field to be used as the Object for the spatial. The object, latitude, and longitude determine the way a spatial map is displayed.
Select the options for Tracking Events. This field determines the proximity of the location for the pattern. The possible values are:
- Near — whenever a new event occurs in the defined spatial region defined by the specified latitude and longitude.
- Enter — whenever an existing event enters the spatial region defined by the specified latitude and longitude.
- Exit — whenever an event exits the spatial region defined by the specified latitude and longitude.
- Stay — when an event stays (the duration is specified in step 11) in the spatial region defined by the specified latitude and longitude.
Select an ID of the Coordinate System. The possible values are:
- 3857 — also known as WGS 84/Pseudo-Mercator - is a geodetic projected Cartesian 2d coordinate system
- 8307 — also known as Longitude/Latitude (WGS 84) - is an Elipsoidal 2d coordinate system. This coordinate system is introduced by Oracle and is used by default.
Enter a numerical value greater than zero for the Distance Buffer. Use the increment or decrement icons to increase or decrease the values. Select a suitable unit for the distance. This value determines the buffer range for calculating the spatial distance when you are tracking an even with a Near parameter.

A buffer usually defines the +/- range for the base value.

Note:
This field is enabled only when you select Near in Tracking Events.
Enter a numerical value greater than zero for the Stay Duration. Use the increment or decrement icons to increase or decrease the values. Select a suitable time unit. This value is used when you select the Stay option in Tracking Events. An event that stays for the specified Stay Duration will be detected.

Note:
This field is enabled only when you select Stay in Tracking Events.

The pattern is visually represented in the form of a map based on the data you have entered/selected.

7.22 Creating Detect Missing Heartbeat Pattern

Use this pattern to detect when an expected event does not occur within a specific time window. For example, use this pattern in circumstances when the next heartbeat event is missing.

To create a Detect Missing Heartbeat pattern:

Navigate to the Catalog.
Select Create New Item > Patterns > Detect Missing Heartbeat. You can also click Patterns and select Detect Missing Heartbeat. The create pattern screen appears as shown below:

Figure 7-26 Create Detect Missing Heartbeat Pattern

Description of "Figure 7-26 Create Detect Missing Heartbeat Pattern"
Select an Event Stream.
Select a field for Partition Criteria.
Enter a numerical value greater than zero for Heartbeat Interval. Use the increment or decrement icons to increase or decrease the value. Select a suitable time unit.

This value indicates the interval within which you want to detect a missing heartbeat.

The pattern is visually represented based on the data you have entered/selected.

7.23 Creating Left Outer Pattern

Use this pattern to enrich original stream events with the data from external reference or another stream. If there are no records in the reference/stream then stream shows null values as the output.

To create Left Outer Join pattern:

Navigate to the Catalog.
Select Create New Item > Pattern > Left Outer Join. You can also click Patterns and select Left Outer Join. The create pattern screen appears as shown below:

Figure 7-27 Create Left Outer Join Pattern

Description of "Figure 7-27 Create Left Outer Join Pattern"
Select a Primary Stream.
Select an Enriching Reference/Stream for the Primary Stream.
Select a Primary Stream Correlation Criteria. This value acts as the correlation criteria for the primary stream.
Select an Enriching Reference/Stream Correlation Criteria for the Primary Stream Correlation Criteria.
Enter a numerical value greater than zero for the Window Range of the Primary Stream. You can use the increment or decrement icons to increase or decrease the value. Select a suitable time unit. This value indicates the window for the primary stream.
Enter a numerical value greater than zero for the Window Slide of the Primary Stream. You can use the increment or decrement icons to increase or decrease the value. Select a suitable time unit. This value indicates the slide for the primary stream.
Enter a numerical value greater than zero for the Window Range of the Enriching Stream. You can use the increment or decrement icons to increase or decrease the value. Select a suitable time unit. This value indicates the window for the enriching/secondary stream.
Enter a numerical value greater than zero for the Window Slide of the Enriching Stream. You can use the increment or decrement icons to increase or decrease the value. Select a suitable time unit. This value indicates the slide for the enriching/secondary stream.

The pattern is visually represented based on the data you have entered/selected.