Data Quality Assessment for Process-Oriented Data

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,...).


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.3.1 by Niels Martin, a year ago

Report a bug at

Browse source code at

Authors: Niels Martin [aut, cre] , Greg Van Houdt [ctb] , Gert Janssenswillen [ctb]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports dplyr, lubridate, stringdist, stringr, tidyr, xesreadR, rlang, bupaR, readr, edeaR, magrittr, purrr, glue, miniUI, shiny

Suggests knitr, rmarkdown

See at CRAN