backend.conformance_checking.resource_based

Contains functionality for resource-based conformance checking.

This module defines the ResourceBased class which uses PM4Py to discover resource-based conformance checking metrics from event logs.

Attributes

SocialNetworkAnalysisType

Classes

ResourceBased

Represents the resource-based conformance checking metrics of a log.

Module Contents

type backend.conformance_checking.resource_based.SocialNetworkAnalysisType = Dict[Tuple[str, str], float]
class backend.conformance_checking.resource_based.ResourceBased(log: pandas.DataFrame, case_id_col: str | None = None, activity_col: str | None = None, timestamp_col: str | None = None, resource_col: str | None = None, group_col: str | None = None)

Represents the resource-based conformance checking metrics of a log.

log

The event log.

case_id_col

The name of the Case ID column. Only needed if the log is read as a csv file.

Type:

optional

activity_col

The name of the Activity column. Only needed if the log is read as a csv file.

Type:

optional

timestamp_col

The name of the Timestamp column. Only needed if the log is read as a csv file.

Type:

optional

resource_col

The name of the Resource column. Only needed if the log is read as a csv file.

Type:

optional

_handover_of_work

The Handover of Work metric. Defaults to None.

_subcontracting

The Subcontracting metric. Defaults to None.

_working_together

The Working Together metric. Defaults to None.

_similar_activities

The Similar Activities metric. Defaults to None.

_organizational_roles

The Organizational Roles of the log. Defaults to None.

_organizational_diagnostics

The Organizational Diagnostics of the log. Defaults to None.

log
case_id_col: str | None = None
activity_col: str | None = None
timestamp_col: str | None = None
resource_col: str | None = None
group_col: str | None = None
compute_handover_of_work() → None

Calculates the Handover of Work metric.

The Handover of Work metric measures how many times an individual is followed by another individual in the execution of a business process. It is stored in a dictionary where the keys are tuples of two individuals and the values are the number of times the first individual is followed by the second individual in the execution of a business process.

get_handover_of_work_values() → SocialNetworkAnalysisType

Returns the Handover of Work metric.

The Handover of Work metric is a dictionary where the keys are tuples of two individuals and the values are the number of times the first individual is followed by the second individual in the execution of a business process.

Returns:

The Handover of Work metric.

Raises:

ValueError – If the Handover of Work values have not been calculated yet.

is_handover_of_work_directed() → bool

Checks if the Handover of Work metric is directed.

Returns:

True if the Handover of Work metric is directed, False otherwise.

compute_subcontracting() → None

Calculates the Subcontracting metric.

The Subcontracting metric calculates how many times the work of an individual is interleaved by the work of another individual, only to eventually “return” to the original individual. It is stored in a dictionary where the keys are tuples of two individuals and the values are the number of times the first individual is interleaved by the second individual in the execution of a business process.

get_subcontracting_values() → SocialNetworkAnalysisType

Returns the Subcontracting metric.

The Subcontracting metric is a dictionary where the keys are tuples of two individuals and the values are the number of times the first individual is interleaved by the second individual in the execution of a business process.

Returns:

The Subcontracting metric.

Raises:

ValueError – If the Subcontracting values have not been calculated yet.

is_subcontracting_directed() → bool

Checks if the Subcontracting metric is directed.

Returns:

True if the Subcontracting metric is directed, False otherwise.

Raises:

ValueError – If the Subcontracting values have not been calculated yet.

compute_working_together() → None

Calculates the Working Together metric.

The Working Together metric calculates how many times two individuals work together to resolve a process instance. It is stored in a dictionary where the keys are tuples of two individuals and the values are the number of times the two individuals worked together to resolve a process instance.

get_working_together_values() → SocialNetworkAnalysisType

Returns the Working Together metric.

The Working Together metric is a dictionary where the keys are tuples of two individuals and the values are the number of times the two individuals worked together to resolve a process instance.

Returns:

The Working Together metric.

Raises:

ValueError – If the Working Together values have not been calculated yet.

is_working_together_directed() → bool

Checks if the Working Together metric is directed.

Returns:

True if the Working Together metric is directed, False otherwise.

Raises:

ValueError – If the Working Together values have not been calculated yet.

compute_similar_activities() → None

Calculates the Similar Activities metric.

The Similar Activities metric calculates how similar the work patterns are between two individuals. It is stored in a dictionary where the keys are tuples of two individuals and the values are the similarity score between the two individuals.

get_similar_activities_values() → SocialNetworkAnalysisType

Returns the Similar Activities metric.

The Similar Activities metric is a dictionary where the keys are tuples of two individuals and the values are the similarity score between the two individuals.

Returns:

The Similar Activities metric.

Raises:

ValueError – If the Similar Activities values have not been calculated yet.

is_similar_activities_directed() → bool

Checks if the Similar Activities metric is directed.

Returns:

True if the Similar Activities metric is directed, False otherwise.

Raises:

ValueError – If the Similar Activities values have not been calculated yet.

compute_organizational_roles() → None

Calculates the organizational roles.

A role is a set of activities in the log that are executed by a similar (multi)set of resources. Hence, it is a specific function within an organization. Grouping the activities into roles can help:

  • In understanding which activities are executed by which roles,

  • By understanding roles themselves (the numerosity of resources for

    a single activity may not provide enough explanation).

Initially, each activity corresponds to a different role and is associated with the multiset of its originators. After that, roles are merged according to their similarity until no more merges are possible.

The information about the roles is stored as a semi-structured list of activity groups, where each group associates a list of activities with a dictionary of originators and their corresponding importance scores.

get_organizational_roles() → List[Dict[str, Any]]

Returns the organizational roles.

The organizational roles are stored as a semi-structured list of activity groups, where each group associates a list of activities with a dictionary of originators and their corresponding importance scores.

Returns:

A list of dictionaries, where each dictionary contains the activities and their corresponding originators’ importance scores. Each dictionary has the following structure: {

”activities”: List[str], # List of activities in the role “originators_importance”: Dict[str, float]

# Dictionary of originators and their importance scores

}

Raises:

ValueError – If the organizational roles have not been calculated yet.

get_number_of_distinct_activities(start_time: str, end_time: str, resource: str) → int

Calculates the number of distinct activities.

Number of distinct activities done by a resource in a given time interval [t1, t2).

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the number of distinct activities.

Returns:

An integer indicating the number of distinct activities done by the resource in the given time interval.

get_activity_frequency(start_time: str, end_time: str, resource: str, activity: str) → float

Calculates the activity frequency.

Fraction of completions of a given activity a by a given resource r during a given time slot [t1, t2), with respect to the total number of activity completions by resource r during [t1, t2).

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the activity frequency.

  • activity (str) – The activity for which to calculate the frequency.

Returns:

A float indicating the activity frequency of the given activity by the resource in the given time interval.

get_activity_completions(start_time: str, end_time: str, resource: str) → int

Calculates the activity completions.

The number of activity instances completed by a given resource during a given time slot.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the activity completions.

Returns:

An integer indicating the number of activity instances completed by the resource in the given time interval.

get_case_completions(start_time: str, end_time: str, resource: str) → int

Calculates the case completion.

The number of cases completed during a given time slot in which a given resource was involved.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the case completions.

Returns:

An integer indicating the number of cases completed by the resource in the given time interval.

get_fraction_case_completions(start_time: str, end_time: str, resource: str) → float

Calculates the fraction case completion.

The fraction of cases completed during a given time slot in which a given resource was involved with respect to the total number of cases completed during the time slot.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the fraction case completions.

Returns:

A float indicating the fraction of cases completed by the resource in the given time interval.

get_average_workload(start_time: str, end_time: str, resource: str) → float

Calculates the average workload.

The average number of activities started by a given resource but not completed at a moment in time.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the average workload.

Returns:

A float indicating the average workload of the given resource in the given time interval.

get_multitasking(start_time: str, end_time: str, resource: str) → float

Calculates the multitasking.

The fraction of active time during which a given resource is involved in more than one activity with respect to the resource’s active time.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the multitasking.

Returns:

A float indicating the multitasking of the given resource in the given time interval.

get_average_activity_duration(start_time: str, end_time: str, resource: str, activity: str) → float

Calculates the average activity duration.

The average duration of instances of a given activity completed during a given time slot by a given resource.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the average activity duration.

  • activity (str) – The activity for which to calculate the average duration.

Returns:

A float indicating the average duration of the given activity by the resource in the given time interval.

get_average_case_duration(start_time: str, end_time: str, resource: str) → float

Calculates the average case duration.

The average duration of cases completed during a given time slot in which a given resource was involved.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the average case duration.

Returns:

A float indicating the average duration of cases completed by the resource in the given time interval.

get_interaction_two_resources(start_time: str, end_time: str, resource1: str, resource2: str) → float

Calculates the interaction between two resources.

The number of cases completed during a given time slot in which two given resources were involved.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource1 (str) – The first resource for which to calculate the interaction.

  • resource2 (str) – The second resource for which to calculate the interaction.

Returns:

A float indicating the interaction between the two resources in the given time interval.

get_social_position(start_time: str, end_time: str, resource: str) → float

Calculates the social position.

The fraction of resources involved in the same cases with a given resource during a given time slot with respect to the total number of resources active during the time slot.

Parameters:

  • start_time (str) – The start time of the interval.

  • end_time (str) – The end time of the interval.

  • resource (str) – The resource for which to calculate the social position.

Returns:

A float indicating the social position of the given resource in the given time interval.

compute_organizational_diagnostics() → None

Calculates the organizational diagnostics.

Provides the local diagnostics for the organizational model starting from a log object and considering the resource group specified by the attribute. It is stored in a dictionary where the keys are the names of the group-related metrics and the values are the corresponding diagnostic values.

Raises:

ValueError – If the resource column name is not provided.

get_group_relative_focus() → Dict[str, Dict[str, float]]

Returns the Group Relative Focus metric.

The Group Relative Focus metric specifies for a given work how much a resource group performed this type of work compared to the overall workload of the group. It can be used to measure how the workload of a resource group is distributed over different types of work, i.e., work diversification of the group.

Returns:

A dictionary where the keys are the names of the resources and the values are dictionaries containing activity names and the Group Relative Focus metric.

Raises:

  • ValueError – If the organizational diagnostics have not been

  • calculated yet. –

get_group_relative_stake() → Dict[str, Dict[str, float]]

Returns the Group Relative Stake metric.

The Group Relative Stake metric specifies for a given work how much this type of work was performed by a certain resource group among all groups. It can be used to measure how the workload devoted to a certain type of work is distributed over resource groups in an organizational model, i.e., work participation by different groups.

Returns:

A dictionary where the keys are the names of the resources and the values are dictionaries containing activity names and the Group Relative Stake metric.

Raises:

  • ValueError – If the organizational diagnostics have not been

  • calculated yet. –

get_group_coverage() → Dict[str, Dict[str, float]]

Returns the Group Coverage metric.

The Group Coverage metric with respect to a given type of work, specifies the proportion of members of a resource group that performed this type of work.

Returns:

A dictionary where the keys are the names of the resources and the values are dictionaries containing resources and the Group Coverage metric.

Raises:

  • ValueError – If the organizational diagnostics have not been

  • calculated yet. –

get_group_member_contribution() → Dict[str, Dict[str, Dict[str, int]]]

Returns the Group Member Contribution metric.

The Group Member Contribution metric of a member of a resource group with respect to a given type of work specifies how much of this type of work by the group was performed by the member. It can be used to measure how the workload of the entire group devoted to a certain type of work is distributed over the group members.

Returns:

A dictionary where the keys are the names of the resources and the values are dictionaries containing resources and the Group Member Contribution metric.

Raises:

  • ValueError – If the organizational diagnostics have not been

  • calculated yet. –