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

fix function

<dl><dt>detected_problems</dt>
<dd>Output of a detect_ function. Currently supported: detect_resource_inconsistencies.</dd>
<dt>...</dt>
<dd>Additionals parameters, depending on type of anomalies to fix.</dd></dl>

Arguments

Fix problems — fix

<dl>

<dt>detected_problems</dt>
<dd>Output of a detect_ function. Currently supported: detect_resource_inconsistencies.</dd>


<dt>...</dt>
<dd>Additionals parameters, depending on type of anomalies to fix.</dd>

</dl>

fix: Fix problems

Description

Usage

Value

Arguments