2025-02-172025-02-172023Resinas Arias de Reyna, M., Río Ortega, A.d. y Van der Aa, H. (2023). From Text to Performance Measurement: Automatically Computing Process Performance Using Textual Descriptions and Event Logs. En 21st International Conference, BPM 2023. Volum. 14159 LNCS. Lecture Notes in Computer Science (266-283), Utrecht, The Netherlands: Springer Nature.0302-97431611-3349https://hdl.handle.net/11441/168792Process performance measurement assesses how well a process is running, covering various dimensions such as time, cost, and quality. This task involves the definition of measurable Process Performance Indicators (PPIs), which in many cases are calculated based on data recorded in an event log. An inhibitor of effective performance analysis is that establishing PPI definitions measurable from event logs is highly complex, because it requires process analytical expertise, as well as in-depth knowledge about the structure and contents of the available event data. Given that managers typically do not have such knowledge, this means that those stakeholders that are generally most interested in measuring process performance cannot do so in a convenient manner. Recognizing this, we bridge this gap by proposing an approach for the measurement of process performance based on textual descriptions and event logs, which combines state-of-the-art natural language processing techniques with matching strategies that are tailored to the task at hand. Evaluation experiments using textual descriptions provided by both industry and academic users demonstrate the accuracy of our approach.application/pdf18 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Process performance measurementProcess miningNatural language processingMatchinginfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess10.1007/978-3-031-41620-0_16