filter_endpoints: Filter Start and End Activities

Description

Filters the log based on a provided set of start and end activities

The filter_endpoints method filters cases based on the first and last activity label. It can be used in two ways: by specifying vectors with allowed start activities and/or allowed end activities, or by specifying a percentile. In the latter case, the percentile value will be used as a cut off. For example, when set to 0.9, it will select the most common endpoint pairs which together cover at least 90% of the cases, and filter the log accordingly.

Usage

filter_endpoints(
  log,
  start_activities = NULL,
  end_activities = NULL,
  percentage = NULL,
  reverse = FALSE
)
# S3 method for log
filter_endpoints(
  log,
  start_activities = NULL,
  end_activities = NULL,
  percentage = NULL,
  reverse = FALSE
)
# S3 method for grouped_log
filter_endpoints(
  log,
  start_activities = NULL,
  end_activities = NULL,
  percentage = NULL,
  reverse = FALSE
)
ifilter_endpoints(log)

Value

When given an object of type log, it will return a filtered log. When given an object of type grouped_log, the filter will be applied in a stratified way (i.e. each separately for each group). The returned log will be grouped on the same variables as the original log.

Arguments

log: log: Object of class log or derivatives (grouped_log, eventlog, activitylog, etc.).
start_activities, end_activities: character vector (default NULL): A vector of activity identifiers, or NULL.
percentage: numeric (default NULL): A percentage p to be used as percentile cut off. When this is used, the most common endpoint-pairs will be selected until at least the p% of the cases are selected.
reverse: logical (default FALSE): Indicating whether the selection should be reversed.

Methods (by class)

filter_endpoints(log): Filters cases for a log.
filter_endpoints(grouped_log): Filters cases for a grouped_log.

Functions

ifilter_endpoints(): Filter interactively

References

Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.

Other filters: filter_activity(), filter_activity_frequency(), filter_activity_instance(), filter_activity_presence(), filter_case(), filter_case_condition(), filter_endpoints_condition(), filter_flow_time(), filter_idle_time(), filter_infrequent_flows(), filter_lifecycle(), filter_lifecycle_presence(), filter_precedence(), filter_precedence_condition(), filter_precedence_resource(), filter_processing_time(), filter_resource(), filter_resource_frequency(), filter_throughput_time(), filter_time_period(), filter_trace(), filter_trace_frequency(), filter_trace_length(), filter_trim(), filter_trim_lifecycle()