filter_anomalies

Function that filters detected anomalies from the activity log

Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...).

Niels Martin

daqapo

Data Quality Assessment for Process-Oriented Data

Greg Van Houdt

Gert Janssenswillen

filter_anomalies function

<dl><dt>activity_log</dt>
<dd>The activity log (renamed/formatted using functions rename_activity_log and convert_timestamp_format)</dd>
<dt>anomaly_log</dt>
<dd>The anomaly log generated from the different DAQAPO tests</dd></dl>

Arguments

Filter anomalies from the activity log — filter_anomalies

<dl>

<dt>activity_log</dt>
<dd>The activity log (renamed/formatted using functions rename_activity_log and convert_timestamp_format)</dd>


<dt>anomaly_log</dt>
<dd>The anomaly log generated from the different DAQAPO tests</dd>

</dl>

filter_anomalies: Filter anomalies from the activity log

Description

Usage

Value

Arguments