Ordino: “to organize”; R: “resources”
OrdinoR is a Python toolkit for organizational model mining, built upon our recent research and capable of supporting the discovery, evaluation, and analysis of organizational models based on event logs. It addresses several critical gaps in the academic literature on the topic, and opens up many possibilities for future research as well as applications, for example, conformance checking of organizational models.
Successful human resource management plays a crucial part in building organizational effectiveness, which relies on decision-makers understanding how their employees act in groups to achieve organizational outcomes. Building this essential HR capability requires seasoned expertise of managers, empowered by the presence of accurate and timely information about the workforce.
Process mining [vdaalst2016] offers useful tools to derive organizational models [song2008] from business process execution data (notably in the form of event logs). These models can capture workforce-related knowledge and provide organizations with insights into their structures and staff deployment.
How to Use¶
OrdinoR can be used in two ways.
Using the example applications¶
You are welcome to use the programs and tools developed as part of our research outcomes. For instance, “Arya” is a webapp providing a graphical interface to perform organizational model mining with OrdinoR. There are also command-line programs developed mainly for the purpose of conducting experiments in our research.
For more information on what’s available and how to use the applications, please visit Examples.
Developing new applications¶
The OrdinoR library is built to be extensible. Users are welcome to develop their own approaches for organizational model mining, either through configuring the examples provided, or through creating new methods/modules that extend the OrdinoR library.
We are currently working on a structured reference to the API.