Root Causes - Revision history

Ollvihe at 10:48, 13 November 2025

2025-11-13T10:48:15Z

← Older revision		Revision as of 10:48, 13 November 2025
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	Root Causes Analysis explains why the selected phenomen occurs ~~using~~ the correlating case attribute values in the model. For example, you might select cases that contain certain undesired event (in the flowchart) and analyze using the Root Causes Analysis, which case attributes and their value have a connection to this undesired behavior. There might be causalities behind the correlations, which would then help to find ''root causes'' for the occurrence of the observed phenomenon. There is also an improved [[AI_Root_Causes\|AI Root Causes]] analysis available.		Root Causes Analysis explains why the selected phenomen occurs based on the correlating case attribute values in the model. For example, you might select cases that contain certain undesired event (in the flowchart) and analyze using the Root Causes Analysis, which case attributes and their value have a connection to this undesired behavior. There might be causalities behind the correlations, which would then help to find ''root causes'' for the occurrence of the observed phenomenon. There is also an improved [[AI_Root_Causes\|AI Root Causes]] analysis available.

	In the Root Causes one row represents cases with one case attribute value. The right-most colored column shows the correlation level between the selected cases and the case attribute value that the row represents.		In the Root Causes one row represents cases with one case attribute value. The right-most colored column shows the correlation level between the selected cases and the case attribute value that the row represents.

Ollvihe at 10:47, 13 November 2025

2025-11-13T10:47:59Z

← Older revision		Revision as of 10:47, 13 November 2025
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	Root Causes ~~shows correlations between cases~~ selected in the ~~Control Window and~~ case attribute values in the model. For example, you might select cases that contain certain undesired event (in the flowchart) and analyze using the Root Causes, which case attributes and their value have a connection to this undesired behavior. There might be causalities behind the correlations, which would then help to find ''root causes'' for the occurrence of the observed phenomenon. There is also an improved [[AI_Root_Causes\|AI Root Causes]] analysis available.		Root Causes Analysis explains why the selected phenomen occurs using the correlating case attribute values in the model. For example, you might select cases that contain certain undesired event (in the flowchart) and analyze using the Root Causes Analysis, which case attributes and their value have a connection to this undesired behavior. There might be causalities behind the correlations, which would then help to find ''root causes'' for the occurrence of the observed phenomenon. There is also an improved [[AI_Root_Causes\|AI Root Causes]] analysis available.

	In the Root Causes one row represents cases with one case attribute value. The right-most colored column shows the correlation level between the selected cases and the case attribute value that the row represents.		In the Root Causes one row represents cases with one case attribute value. The right-most colored column shows the correlation level between the selected cases and the case attribute value that the row represents.

TeeHiet: TK-61818

2025-11-11T12:49:28Z

TK-61818

← Older revision		Revision as of 12:49, 11 November 2025
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	Root Causes shows correlations between cases selected in the Control Window and case attribute values in the model. For example, you might select cases that contain certain undesired event (in the flowchart) and analyze using the Root Causes, which case attributes and their value have a connection to this undesired behavior. There might be causalities behind the correlations, which would then help to find ''root causes'' for the occurrence of the observed phenomenon. There is also ~~a more~~ improved [[AI_Root_Causes\|AI Root Causes]] analysis available.		Root Causes shows correlations between cases selected in the Control Window and case attribute values in the model. For example, you might select cases that contain certain undesired event (in the flowchart) and analyze using the Root Causes, which case attributes and their value have a connection to this undesired behavior. There might be causalities behind the correlations, which would then help to find ''root causes'' for the occurrence of the observed phenomenon. There is also an improved [[AI_Root_Causes\|AI Root Causes]] analysis available.

	In the Root Causes one row represents cases with one case attribute value. The right-most colored column shows the correlation level between the selected cases and the case attribute value that the row represents.		In the Root Causes one row represents cases with one case attribute value. The right-most colored column shows the correlation level between the selected cases and the case attribute value that the row represents.

TeeHiet: TK-61818

2025-11-11T12:35:40Z

TK-61818

← Older revision		Revision as of 12:35, 11 November 2025
Line 1:		Line 1:
	Root Causes shows correlations between cases selected in the Control Window and case attribute values in the model. For example, you might select cases that contain certain undesired event (in the flowchart) and analyze using the Root Causes, which case attributes and their value have a connection to this undesired behavior. There might be causalities behind the correlations, which would then help to find ''root causes'' for the occurrence of the observed phenomenon.		Root Causes shows correlations between cases selected in the Control Window and case attribute values in the model. For example, you might select cases that contain certain undesired event (in the flowchart) and analyze using the Root Causes, which case attributes and their value have a connection to this undesired behavior. There might be causalities behind the correlations, which would then help to find ''root causes'' for the occurrence of the observed phenomenon. There is also a more improved [[AI_Root_Causes\|AI Root Causes]] analysis available.

	In the Root Causes one row represents cases with one case attribute value. The right-most colored column shows the correlation level between the selected cases and the case attribute value that the row represents.		In the Root Causes one row represents cases with one case attribute value. The right-most colored column shows the correlation level between the selected cases and the case attribute value that the row represents.

Ollvihe: /* Root causes in flowchart */

2023-05-01T15:02:12Z

Root causes in flowchart

Ollvihe: /* Root causes in flowchart */

2023-05-01T14:09:31Z

Root causes in flowchart

← Older revision		Revision as of 14:09, 1 May 2023
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	* The stronger the red color, the stronger the '''positive correlation''' between the selected feature and the event type or flow occurrence		* The stronger the red color, the stronger the '''positive correlation''' between the selected feature and the event type or flow occurrence
	* The stronger the blue color, the stronger the '''negative correlation''' between the selected feature and the event type or flow occurrence		* The stronger the blue color, the stronger the '''negative correlation''' between the selected feature and the event type or flow occurrence
	* If there is no correlation or the correlation is weak, the color is ~~greyish~~.		* If there is no correlation or the correlation is weak, the color is grayish.

	For example, let's say we select exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type ''Shopping Cart Created'' has occurred in 72% of the long cases, where the on average in all cases, ''Shopping Cart Created'' has occurred in 47% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created'' may explain why case durations are long.		For example, let's say we select exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type ''Shopping Cart Created'' has occurred in 72% of the long cases, where the on average in all cases, ''Shopping Cart Created'' has occurred in 47% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created'' may explain why case durations are long.
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	On the other hand, we can see that the event type ''PR Created'' has occurred in 18% of the long cases, where the on average in all cases, ''PR Created'' has occurred in 35% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created'' may explain why case durations are long.		On the other hand, we can see that the event type ''PR Created'' has occurred in 18% of the long cases, where the on average in all cases, ''PR Created'' has occurred in 35% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created'' may explain why case durations are long.

	Same logic applies for the flows:		Same logic applies for the flows: for example the flow between Shopping Cart Approved and PO Item Created, occurred in 78% of the long cases, where the on average in all cases, the flow has occurred in 46% of cases. We may say that this flow (i.e., the fact that PO Item Created occurred directly after Shopping Cart Approved) may explain why case durations are long.

	[[File:Root causes flowchart.png]]		[[File:Root causes flowchart.png]]

	[[Category: QPR ProcessAnalyzer]]		[[Category: QPR ProcessAnalyzer]]

Ollvihe: /* Root causes in flowchart */

2023-05-01T13:33:04Z

Root causes in flowchart

← Older revision		Revision as of 13:33, 1 May 2023
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	Then choose a feature to find root causes for as follows either by:		Then choose a feature to find root causes for as follows either by:

	* Make a selection in a chart and and click the ~~<nowiki>~~'''Show root causes'''~~</nowiki>~~ button.		* Make a selection in a chart and and click the '''Show root causes''' button.
	* Click the red plus button in the header and choose the rule in the opening dialog.		* Click the red plus button in the header and choose the rule in the opening dialog.

	The correlations		The correlations are shown with colors for the event types and flows as follows:
			* The stronger the red color, the stronger the '''positive correlation''' between the selected feature and the event type or flow occurrence
			* The stronger the blue color, the stronger the '''negative correlation''' between the selected feature and the event type or flow occurrence
			* If there is no correlation or the correlation is weak, the color is greyish.

	For example, let~~<nowiki>~~'s say we select exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type ''Shopping Cart Created'' has occurred in 72% of the long cases, where the on average in all cases, ''Shopping Cart Created'' has occurred in 47% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created''~~</nowiki>~~ may explain why case durations are long.		For example, let's say we select exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type ''Shopping Cart Created'' has occurred in 72% of the long cases, where the on average in all cases, ''Shopping Cart Created'' has occurred in 47% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created'' may explain why case durations are long.

	On the other hand, we can see that the event type ~~<nowiki>~~''PR Created'' has occurred in 18% of the long cases, where the on average in all cases, ''PR Created'' has occurred in 35% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created''~~</nowiki>~~ may explain why case durations are long.		On the other hand, we can see that the event type ''PR Created'' has occurred in 18% of the long cases, where the on average in all cases, ''PR Created'' has occurred in 35% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created'' may explain why case durations are long.

			Same logic applies for the flows:

	[[File:Root causes flowchart.png]]		[[File:Root causes flowchart.png]]

	[[Category: QPR ProcessAnalyzer]]		[[Category: QPR ProcessAnalyzer]]

Ollvihe: /* Root causes in flowchart */

2023-05-01T13:29:45Z

Root causes in flowchart

← Older revision		Revision as of 13:29, 1 May 2023
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	== Root causes in flowchart ==		== Root causes in flowchart ==
	Root causes can be visualized in ~~flowcharts that can show~~ how the ~~occurrences of~~ event ~~types~~ and ~~flows~~ correlate with the selected feature. Root causes can be ~~viewed in flowchart as follows~~:		Root causes can be visualized in the flowchart showing how the event type and flow occurrences correlate with the selected feature. Root causes analysis in the flowchart can be opened either by:
	* If tabs ~~above~~ the flowchart are visible click the '''Root causes''' tab.		* If tabs on top of the flowchart are visible click the '''Root causes''' tab.
	* If tabs ~~above~~ the flowchart are not visible, open the flowchart settings and in the '''Presets''' tab, selected '''Root causes'''.		* If tabs on top of the flowchart are not visible, open the flowchart settings and in the '''Presets''' tab, selected '''Root causes'''.

	For example, let's say we select ~~the~~ exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type A has occurred in X% of the long cases, where the on average in all cases, the event type A has occurred in Y cases. We may say ~~then~~ that the occurrence of the ~~event type A~~ may explain why case durations are long.		Then choose a feature to find root causes for as follows either by:

			* Make a selection in a chart and and click the <nowiki>'''Show root causes'''</nowiki> button.
			* Click the red plus button in the header and choose the rule in the opening dialog.

			The correlations

			For example, let<nowiki>'s say we select exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type ''Shopping Cart Created'' has occurred in 72% of the long cases, where the on average in all cases, ''Shopping Cart Created'' has occurred in 47% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created''</nowiki> may explain why case durations are long.

			On the other hand, we can see that the event type <nowiki>''PR Created'' has occurred in 18% of the long cases, where the on average in all cases, ''PR Created'' has occurred in 35% of cases. We may say that the frequency of occurrence of the ''Shopping Cart Created''</nowiki> may explain why case durations are long.

	[[File:Root causes flowchart.png]]		[[File:Root causes flowchart.png]]

	[[Category: QPR ProcessAnalyzer]]		[[Category: QPR ProcessAnalyzer]]

Ollvihe: /* Root causes in flowchart */

2023-05-01T13:10:33Z

Root causes in flowchart

← Older revision		Revision as of 13:10, 1 May 2023
Line 45:		Line 45:

	For example, let's say we select the exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type A has occurred in X% of the long cases, where the on average in all cases, the event type A has occurred in Y cases. We may say then that the occurrence of the event type A may explain why case durations are long.		For example, let's say we select the exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type A has occurred in X% of the long cases, where the on average in all cases, the event type A has occurred in Y cases. We may say then that the occurrence of the event type A may explain why case durations are long.

			[[File:Root causes flowchart.png]]

	[[Category: QPR ProcessAnalyzer]]		[[Category: QPR ProcessAnalyzer]]

Ollvihe: /* Weighted by Case Value Mode */

2023-05-01T01:04:15Z

Weighted by Case Value Mode

← Older revision		Revision as of 01:04, 1 May 2023
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	You can sort the columns in the analysis by clicking on the column headers. To filter the shown content of the table, click on the filter icon.		You can sort the columns in the analysis by clicking on the column headers. To filter the shown content of the table, click on the filter icon.

	~~__NOTOC__~~		== Root causes in flowchart ==
			Root causes can be visualized in flowcharts that can show how the occurrences of event types and flows correlate with the selected feature. Root causes can be viewed in flowchart as follows:
			* If tabs above the flowchart are visible click the '''Root causes''' tab.
			* If tabs above the flowchart are not visible, open the flowchart settings and in the '''Presets''' tab, selected '''Root causes'''.

			For example, let's say we select the exceptionally long case duration as a feature to find root causes for. In the following picture we can see that the event type A has occurred in X% of the long cases, where the on average in all cases, the event type A has occurred in Y cases. We may say then that the occurrence of the event type A may explain why case durations are long.

	[[Category: QPR ProcessAnalyzer]]		[[Category: QPR ProcessAnalyzer]]

← Older revision		Revision as of 15:02, 1 May 2023
Line 40:		Line 40:

	== Root causes in flowchart ==		== Root causes in flowchart ==
	Root causes can be visualized in the flowchart showing how the event ~~type~~ and flow occurrences correlate with the selected feature. Root causes analysis in the flowchart can be opened either by:		Root causes can be visualized in the flowchart showing how the event and flow occurrences correlate with the selected feature. Root causes analysis in the flowchart can be opened either by clicking the '''Root causes''' tab (if tabs on top of the flowchart are visible) or opening the flowchart settings and in the '''Presets''' tab, selecting '''Root causes''' (if tabs on top of the flowchart are not visible). Then choose a feature to find root causes for using either of the following methods:
	* If tabs on top of the flowchart are visible click the '''Root causes''' tab.
	* If tabs on top of the flowchart are ~~not~~ visible~~, open~~ the flowchart settings and in the '''Presets''' tab, ~~selected~~ '''Root causes'''.

	Then choose a feature to find root causes for ~~as follows~~ either by:

	* Make a selection in a chart and and click the '''Show root causes''' button.		* Make a selection in a chart and and click the '''Show root causes''' button.
	* Click the red plus button in the header and choose the rule in the opening dialog.		* Click the red plus button in the header and choose the rule in the opening dialog.

	~~The~~ correlations are ~~shown~~ with colors for the event types and flows as follows:		In the flowchart, correlations are visualized with colors for the event types and flows as follows:
	* The stronger the red color, the ~~stronger~~ the '''positive correlation''' between the selected feature and the event type or flow occurrence		* The stronger the red color, the higher the '''positive correlation''' between the selected feature and the event type or flow occurrence.
	* The stronger the blue color, the ~~stronger~~ the '''negative correlation''' between the selected feature and the event type or flow occurrence		* The stronger the blue color, the higher the '''negative correlation''' between the selected feature and the event type or flow occurrence.
	* If there is no correlation or the correlation is weak, the color is grayish.		* If there is no correlation or the correlation is weak, the color is grayish.

	~~For example, let~~'s say we select exceptionally long ~~case~~ duration as a feature to find root causes for. In the following picture we can see that the event type ''Shopping Cart Created'' has occurred in 72% of the long cases, where ~~the~~ on average in all cases, ''Shopping Cart Created'' has occurred in 47% of cases. We may say that the ~~frequency of~~ occurrence of the ''Shopping Cart Created'' may explain why ~~case durations are long~~.		Example

			Let's say we select cases with exceptionally long duration as a feature to find root causes for the long durations. In the following picture we can see that the event type ''Shopping Cart Created'' has occurred in 78% of the long cases, where as on average among all cases, ''Shopping Cart Created'' has occurred in 47% of cases. We may say that the occurrence of the ''Shopping Cart Created'' events may explain why some cases take longer.

	On the other hand, we can see that the event type ''PR Created'' has occurred in 18% of the long cases, where ~~the~~ on average in all cases, ''PR Created'' has occurred in 35% of cases. We may say that the ~~frequency of~~ occurrence of the ''Shopping Cart Created'' may ~~explain why case durations are long~~.		On the other hand, we can see that the event type ''PR Created'' has occurred in 14% of the long cases, where as on average among all cases, ''PR Created'' has occurred in 35% of cases. We may say that the occurrence of the ''Shopping Cart Created'' may contribute to completing some cases in shorter time.

	Same logic applies for the flows: for example the flow between Shopping Cart Approved and PO Item Created, occurred in 78% of the long cases, where ~~the~~ on average in all cases, the flow has occurred in 46% of cases. We may say that this flow (i.e., the fact that PO Item Created ~~occurred~~ directly after Shopping Cart Approved) may explain why ~~case durations are long~~.		Same logic applies for the flows: for example the flow between ''Shopping Cart Approved'' and ''PO Item Created'' occurred in 78% of the long cases, where as on average among all cases, the flow has occurred in 46% of cases. We may say that this flow (i.e., the fact that ''PO Item Created'' occurs directly after ''Shopping Cart Approved'') may explain why some cases take longer.

	[[File:Root causes flowchart.png]]		[[File:Root causes flowchart.png]]

	[[Category: QPR ProcessAnalyzer]]		[[Category: QPR ProcessAnalyzer]]