Authors: Gaël Bernard, Periklis Andritsos
Tags: 2020, conceptual modeling
We consider truncated traces, which are incomplete sequences of events. This typically happens when dealing with streaming data or when the event log extraction process cuts the end of the trace. The existence of truncated traces in event logs and their negative impacts on process mining outcomes have been widely acknowledged in the literature. Still, there is a lack of research on algorithms to detect them. We propose the Truncated Trace Classifier (TTC), an algorithm that distinguishes truncated traces from the ones that are not truncated. We benchmark 5 TTC implementations that use either LSTM or XGBOOST on 13 real-life event logs. Accurate TTCs have great potential. In fact, filtering truncated traces before applying a process discovery algorithm greatly improves the precision of the discovered process models, by 9.1%. Moreover, we show that TTCs increase the accuracy of a next event prediction algorithm by up to 7.5%.Read the full paper here: https://link.springer.com/chapter/10.1007/978-3-030-49418-6_10
