Chart Basics - Revision history

Lasuusi at 12:32, 24 September 2020

2020-09-24T12:32:13Z

OllKomu: /* Root causes */

2020-09-14T14:01:47Z

Root causes

← Older revision		Revision as of 14:01, 14 September 2020
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	The idea of Root-cause analysis is to see whether some Subsets of data (=Case Attributes) correlate with any selected process behavior. Moreover, to provide insights where to focus business initiatives to maximize or minimize the selected process behavior.		The idea of Root-cause analysis is to see whether some Subsets of data (=Case Attributes) correlate with any selected process behavior. Moreover, to provide insights where to focus business initiatives to maximize or minimize the selected process behavior.

	'''For example:''' Order Price Change ratio is on average 17%. Is it 17% through all the case attributes such as Purchasing Country or is the Order Price Change ratio potentially 2% in Country X and 9% in Country Z. -> Best practices from Country X to be rolled into Country Z operations.		'''For example:''' Order Price Change ratio is on average 17%. Is it 17% through all the case attributes such as Purchasing Country or is the Order Price Change ratio potentially 2% in Country X and 9% in Country Z. -> Best practices from Country X to be rolled into Country Z operations.

OllKomu: /* Root causes */

2020-09-14T14:01:38Z

Root causes

← Older revision		Revision as of 14:01, 14 September 2020
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	The idea of Root-cause analysis is to see whether some Subsets of data (=Case Attributes) correlate with any selected process behavior. Moreover, to provide insights where to focus business initiatives to maximize or minimize the selected process behavior.		The idea of Root-cause analysis is to see whether some Subsets of data (=Case Attributes) correlate with any selected process behavior. Moreover, to provide insights where to focus business initiatives to maximize or minimize the selected process behavior.
	For example: Order Price Change ratio is on average 17%. Is it 17% through all the case attributes such as Purchasing Country or is the Order Price Change ratio potentially 2% in Country X and 9% in Country Z. -> Best practices from Country X to be rolled into Country Z operations.		'''For example:''' Order Price Change ratio is on average 17%. Is it 17% through all the case attributes such as Purchasing Country or is the Order Price Change ratio potentially 2% in Country X and 9% in Country Z. -> Best practices from Country X to be rolled into Country Z operations.

	* '''Header:''' Average of the analyzed feature in whole data model. Used as a benchmark for all the data subsets e.g. dimensioning with all case attribute values and seeing whether there are any subsets of data which have a higher or lower average.		* '''Header:''' Average of the analyzed feature in whole data model. Used as a benchmark for all the data subsets e.g. dimensioning with all case attribute values and seeing whether there are any subsets of data which have a higher or lower average.
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	* '''3rd Column:''' Case count within the Subset of data (see column #2), 24 700 cases		* '''3rd Column:''' Case count within the Subset of data (see column #2), 24 700 cases
	* '''4th Column:''' Percentage of the cases having the analyzed feature in the concerned subset of data e.g. Case Attribute Value SO: Customer country: United Kingdom has 7.8% of orders having the analyzed features (Order Price Changes). Note. Clearly lower than global average 17%.		* '''4th Column:''' Percentage of the cases having the analyzed feature in the concerned subset of data e.g. Case Attribute Value SO: Customer country: United Kingdom has 7.8% of orders having the analyzed features (Order Price Changes). Note. Clearly lower than global average 17%.
	* '''1st Column:''' Because the subset of data (e.g. concerned case attribute value for example SO: Customer country: United Kingdom) has a lower or a higher percentage of cases having the concerned feature compared to global average, it is responsible for X% of the whole featured effect. In other words, how many cases too many/too little there are in this Data subset compared to global average. Finally, this number of cases is divided by the total number of cases having the analyzed feature to make it a “contribution percentage”. ~~'''~~This means that global average is now 15% lower than it would be if United Kingdom was on a global average level 17% (contracts to 7.8% what it currently has)~~'''~~.		* '''1st Column:''' Because the subset of data (e.g. concerned case attribute value for example SO: Customer country: United Kingdom) has a lower or a higher percentage of cases having the concerned feature compared to global average, it is responsible for X% of the whole featured effect. In other words, how many cases too many/too little there are in this Data subset compared to global average. Finally, this number of cases is divided by the total number of cases having the analyzed feature to make it a “contribution percentage”. This means that global average is now 15% lower than it would be if United Kingdom was on a global average level 17% (contracts to 7.8% what it currently has).

	== Clustering ==		== Clustering ==

OllKomu: /* Root causes */

2020-09-14T14:01:09Z

Root causes

← Older revision		Revision as of 14:01, 14 September 2020
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	* '''3rd Column:''' Case count within the Subset of data (see column #2), 24 700 cases		* '''3rd Column:''' Case count within the Subset of data (see column #2), 24 700 cases
	* '''4th Column:''' Percentage of the cases having the analyzed feature in the concerned subset of data e.g. Case Attribute Value SO: Customer country: United Kingdom has 7.8% of orders having the analyzed features (Order Price Changes). Note. Clearly lower than global average 17%.		* '''4th Column:''' Percentage of the cases having the analyzed feature in the concerned subset of data e.g. Case Attribute Value SO: Customer country: United Kingdom has 7.8% of orders having the analyzed features (Order Price Changes). Note. Clearly lower than global average 17%.
	* '''1st Column:''' Because the subset of data (e.g. concerned case attribute value for example SO: Customer country: United Kingdom) has a lower or a higher percentage of cases having the concerned feature compared to global average, it is responsible for X% of the whole featured effect. In other words, how many cases too many/too little there are in this Data subset compared to global average. Finally, this number of cases is divided by the total number of cases having the analyzed feature to make it a “contribution percentage”. This means that global average is now 15% lower than it would be if United Kingdom was on a global average level 17% (contracts to 7.8% what it currently has).		* '''1st Column:''' Because the subset of data (e.g. concerned case attribute value for example SO: Customer country: United Kingdom) has a lower or a higher percentage of cases having the concerned feature compared to global average, it is responsible for X% of the whole featured effect. In other words, how many cases too many/too little there are in this Data subset compared to global average. Finally, this number of cases is divided by the total number of cases having the analyzed feature to make it a “contribution percentage”. '''This means that global average is now 15% lower than it would be if United Kingdom was on a global average level 17% (contracts to 7.8% what it currently has)'''.

	== Clustering ==		== Clustering ==

OllKomu: /* Root causes */

2020-09-14T14:00:42Z

Root causes

← Older revision		Revision as of 14:00, 14 September 2020
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	* '''3rd Column:''' Case count within the Subset of data (see column #2), 24 700 cases		* '''3rd Column:''' Case count within the Subset of data (see column #2), 24 700 cases
	* '''4th Column:''' Percentage of the cases having the analyzed feature in the concerned subset of data e.g. Case Attribute Value SO: Customer country: United Kingdom has 7.8% of orders having the analyzed features (Order Price Changes). Note. Clearly lower than global average 17%.		* '''4th Column:''' Percentage of the cases having the analyzed feature in the concerned subset of data e.g. Case Attribute Value SO: Customer country: United Kingdom has 7.8% of orders having the analyzed features (Order Price Changes). Note. Clearly lower than global average 17%.
	* '''1st Column:''' Because the subset of data (e.g. concerned case attribute value for example SO: Customer country: United Kingdom) has a lower or a higher percentage of cases having the concerned feature compared to global average, it is responsible for X% of the whole featured effect.		* '''1st Column:''' Because the subset of data (e.g. concerned case attribute value for example SO: Customer country: United Kingdom) has a lower or a higher percentage of cases having the concerned feature compared to global average, it is responsible for X% of the whole featured effect. In other words, how many cases too many/too little there are in this Data subset compared to global average. Finally, this number of cases is divided by the total number of cases having the analyzed feature to make it a “contribution percentage”. This means that global average is now 15% lower than it would be if United Kingdom was on a global average level 17% (contracts to 7.8% what it currently has).
	In other words, how many cases too many/too little there are in this Data subset compared to global average. Finally, this number of cases is divided by the total number of cases having the analyzed feature to make it a “contribution percentage”. This means that global average is now 15% lower than it would be if United Kingdom was on a global average level 17% (contracts to 7.8% what it currently has).


	== Clustering ==		== Clustering ==

OllKomu at 14:00, 14 September 2020

2020-09-14T14:00:03Z

← Older revision		Revision as of 14:00, 14 September 2020
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	For example: Order Price Change ratio is on average 17%. Is it 17% through all the case attributes such as Purchasing Country or is the Order Price Change ratio potentially 2% in Country X and 9% in Country Z. -> Best practices from Country X to be rolled into Country Z operations.		For example: Order Price Change ratio is on average 17%. Is it 17% through all the case attributes such as Purchasing Country or is the Order Price Change ratio potentially 2% in Country X and 9% in Country Z. -> Best practices from Country X to be rolled into Country Z operations.

	Header: Average of the analyzed feature in whole data model. Used as a benchmark for all the data subsets e.g. dimensioning with all case attribute values and seeing whether there are any subsets of data which have a higher or lower average.		* '''Header:''' Average of the analyzed feature in whole data model. Used as a benchmark for all the data subsets e.g. dimensioning with all case attribute values and seeing whether there are any subsets of data which have a higher or lower average.
	2nd Column: Subset of data: Case Attribute + Concerned Case Attribute value e.g. Case Attribute: SO: Customer Country: United Kingdom		* '''2nd Column:''' Subset of data: Case Attribute + Concerned Case Attribute value e.g. Case Attribute: SO: Customer Country: United Kingdom
	3rd Column: Case count within the Subset of data (see column #2), 24 700 cases		* '''3rd Column:''' Case count within the Subset of data (see column #2), 24 700 cases
			* '''4th Column:''' Percentage of the cases having the analyzed feature in the concerned subset of data e.g. Case Attribute Value SO: Customer country: United Kingdom has 7.8% of orders having the analyzed features (Order Price Changes). Note. Clearly lower than global average 17%.
	4th Column: Percentage of the cases having the analyzed feature in the concerned subset of data e.g. Case Attribute Value SO: Customer country: United Kingdom has 7.8% of orders having the analyzed features (Order Price Changes). Note. Clearly lower than global average 17%.		* '''1st Column:''' Because the subset of data (e.g. concerned case attribute value for example SO: Customer country: United Kingdom) has a lower or a higher percentage of cases having the concerned feature compared to global average, it is responsible for X% of the whole featured effect.

	1st Column: Because the subset of data (e.g. concerned case attribute value for example SO: Customer country: United Kingdom) has a lower or a higher percentage of cases having the concerned feature compared to global average, it is responsible for X% of the whole featured effect.
	In other words, how many cases too many/too little there are in this Data subset compared to global average. Finally, this number of cases is divided by the total number of cases having the analyzed feature to make it a “contribution percentage”. This means that global average is now 15% lower than it would be if United Kingdom was on a global average level 17% (contracts to 7.8% what it currently has).		In other words, how many cases too many/too little there are in this Data subset compared to global average. Finally, this number of cases is divided by the total number of cases having the analyzed feature to make it a “contribution percentage”. This means that global average is now 15% lower than it would be if United Kingdom was on a global average level 17% (contracts to 7.8% what it currently has).

OllKomu at 13:59, 14 September 2020

2020-09-14T13:59:03Z

← Older revision		Revision as of 13:59, 14 September 2020
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	*'''St.dev.:''' Standard deviation of the duration of the flow.		*'''St.dev.:''' Standard deviation of the duration of the flow.
	== Root causes ==		== Root causes ==
	[[File:Tabs ~~12rootcauses~~.png\|600px]]		[[File:Tabs 12rootcauses2.png\|600px]]

	The idea of Root-cause analysis is to see whether some Subsets of data (=Case Attributes) correlate with any selected process behavior. Moreover, to provide insights where to focus business initiatives to maximize or minimize the selected process behavior.		The idea of Root-cause analysis is to see whether some Subsets of data (=Case Attributes) correlate with any selected process behavior. Moreover, to provide insights where to focus business initiatives to maximize or minimize the selected process behavior.

OllKomu at 13:56, 14 September 2020

2020-09-14T13:56:58Z

@@ Line 100: / Line 100: @@
 [[File:Tabs 12rootcauses.png|600px]]
-To be updated
+The idea of Root-cause analysis is to see whether some Subsets of data (=Case Attributes) correlate with any selected process behavior. Moreover, to provide insights where to focus business initiatives to maximize or minimize the selected process behavior.
 == Clustering ==
 [[File:Tabs 13clustering.png|600px]]

Lasuusi at 09:04, 31 August 2020

2020-08-31T09:04:06Z

← Older revision		Revision as of 09:04, 31 August 2020
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	*'''Occurrences:''' Count of flow occurrences.		*'''Occurrences:''' Count of flow occurrences.
	'''Looping:''' Looping percentage (repetition per case count). Calculation rule: ''(Occurrences / Cases - 1) 100''.		'''Looping:''' Looping percentage (repetition per case count). Calculation rule: ''(Occurrences / Cases - 1) 100''.
	*'''St.dev.:''' ~~Duration standard~~ deviation of the flow.		*'''St.dev.:''' Standard deviation of the duration of the flow.
	== Root causes ==		== Root causes ==
	[[File:Tabs 12rootcauses.png\|600px]]		[[File:Tabs 12rootcauses.png\|600px]]

Lasuusi at 06:56, 31 August 2020

2020-08-31T06:56:02Z

← Older revision		Revision as of 06:56, 31 August 2020
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	[[File:Tabs 13clustering.png\|600px]]		[[File:Tabs 13clustering.png\|600px]]

	To be ~~updated~~		* '''Feature''' and '''Value''': These two columns list the case attribute and other values that are common to the cases in the cluster.
			* '''Cluster Density %''': Share of cases having this feature value within the cluster (i.e. the number of cases having the value shown on the row in this particular cluster divided by the number of cases in the cluster * 100).
			* '''Total Density %''': Share of cases having this feature value in the whole data set (i.e. the total number of cases having the value shown on the row divided by the total number of cases * 100).
			* '''Contribution %''': Amount of cases that can be explained to belong to this cluster because of this feature value. The scale is such that 0% means that the feature value isn't specific to this cluster and 100% means that all cases belonging to this cluster can be explained by this feature value.

	= Using presets =		= Using presets =

← Older revision		Revision as of 12:32, 24 September 2020
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	[[File:Tabs 1cases.png\|600px]]		[[File:Tabs 1cases.png\|600px]]

			Case analysis shows each individual case within the (filtered) dataset in their own rows:
	*'''Case ID:''' The individual identifier assigned to a case.		*'''Case ID:''' The individual identifier assigned to a case.
	*'''Case start:''' Time stamp of the first event of the case.		*'''Case start:''' Time stamp of the first event of the case.
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	[[File:Tabs 2events.png\|600px]]		[[File:Tabs 2events.png\|600px]]

			Events analysis shows each individual event within the (filtered) dataset in their own rows:
	*'''Case ID:''' The individual identifier assigned to a case.		*'''Case ID:''' The individual identifier assigned to a case.
	*'''Event type:''' Name of the event.		*'''Event type:''' Name of the event.
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	[[File:Tabs 3eventtypes.png\|600px]]		[[File:Tabs 3eventtypes.png\|600px]]

			Event types analysis shows each individual event type within the (filtered) dataset in their own rows with their respective aggregated information:
	*'''Event type:''' Name of the event.		*'''Event type:''' Name of the event.
	*'''Cases:''' Count of cases containing the event type.		*'''Cases:''' Count of cases containing the event type.
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	[[File:Tabs 4caseattributes.png\|600px]]		[[File:Tabs 4caseattributes.png\|600px]]

	Case Attribute profiling visualizes the break down of the case count according to Case Attribute values. You can choose different Case Attributes to analyze by toggling the ~~drop down~~ menu. The Visualization defaults to bar chart which you can change from the "General" tab of the Chart Settings.		Case Attribute profiling visualizes the break down of the case count according to Case Attribute values. You can choose different Case Attributes to analyze by toggling the dropdown menu. The Visualization defaults to bar chart which you can change from the "General" tab of the Chart Settings.

	== Event Attribute Profiling ==		== Event Attribute Profiling ==
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	[[File:Tabs 5eventattributes.png\|600px]]		[[File:Tabs 5eventattributes.png\|600px]]

	Event Attribute profiling visualizes the break down of the event count according to Event Attribute values. You can choose different Event Attributes to analyze by toggling the ~~drop down~~ menu. The Visualization defaults to bar chart which you can change from the "General" tab of the Chart Settings.		Event Attribute profiling visualizes the break down of the event count according to Event Attribute values. You can choose different Event Attributes to analyze by toggling the dropdown menu. The Visualization defaults to bar chart which you can change from the "General" tab of the Chart Settings.
	== Case duration analysis ==		== Case duration analysis ==

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	Event duration analysis breaks down the flow duration between two selected events. You can choose the analysis target by either:		Event duration analysis breaks down the flow duration between two selected events. You can choose the analysis target by either:
	*Selecting a flow from the flowchart.		*Selecting a flow from the flowchart.
	*Toggling the "Start" and "End" ~~drop down~~ menus and selecting the desired events.		*Toggling the "Start" and "End" dropdown menus and selecting the desired events.

	You can choose the granularity of the event durations from the "Time unit" dropdown menu. The Visualization defaults to column chart which you can change from the "General" tab of the Chart Settings.		You can choose the granularity of the event durations from the "Time unit" dropdown menu. The Visualization defaults to column chart which you can change from the "General" tab of the Chart Settings.
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	[[File:Tabs 8casecounts.png\|600px]]		[[File:Tabs 8casecounts.png\|600px]]

	Case Counts analysis breaks down the Case Count based on the starting time of the (filtered) Cases in the data. You can choose the granularity of the time periods from the "Period" ~~drop down~~ menu. The Visualization defaults to column chart which you can change from the "General" tab of the Chart Settings.		Case Counts analysis breaks down the Case Count based on the starting time of the (filtered) Cases in the data. You can choose the granularity of the time periods from the "Period" dropdown menu. The Visualization defaults to column chart which you can change from the "General" tab of the Chart Settings.
	== Event counts ==		== Event counts ==
	[[File:Tabs 9eventcounts.png\|600px]]		[[File:Tabs 9eventcounts.png\|600px]]

	Event Counts analysis breaks down the Event Count based on the starting time of the (filtered) Events in the data. You can choose the granularity of the time periods from the "Period" ~~drop down~~ menu. The Visualization defaults to column chart which you can change from the "General" tab of the Chart Settings.		Event Counts analysis breaks down the Event Count based on the starting time of the (filtered) Events in the data. You can choose the granularity of the time periods from the "Period" dropdown menu. The Visualization defaults to column chart which you can change from the "General" tab of the Chart Settings.
	== Variations ==		== Variations ==
	[[File:Tabs 10variations.png\|600px]]		[[File:Tabs 10variations.png\|600px]]
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	[[File:Tabs 11flows.png\|600px]]		[[File:Tabs 11flows.png\|600px]]

	Flows analysis aggregates all start event to end event flows and sorts them in a descending order of Average duration of the flows.		Flows analysis aggregates all start event to end event flows and sorts them in a descending order of Average duration of the flows:
	*'''Start:''' Name of the start event of the flow.		*'''Start:''' Name of the start event of the flow.
	*'''End:''' Name of the end event of the flow.		*'''End:''' Name of the end event of the flow.