Learning Effective Neural Nets for Outcome Prediction from Partially Labelled Log Data The problem of inducing a model for forecasting the outcome of an ongoing process instance from historical log traces has attracted notable attention in the field of Process Mining. Approaches based on deep neural networks have become popular in this context, as a more effective alternative to previous featurebased outcomeprediction methods. However, these approaches rely on a pure supervised learning scheme, and unfit many reallife scenarios where the outcome of (fully unfolded) training traces must be provided by experts. Indeed, since in such a scenario only a small amount of labeled traces are usually given, there is a risk that an inaccurate or overfitting model is discovered. To overcome these issues, a novel outcomediscovery approach is proposed here, which leverages a finetuning strategy that learns generalenough trace representations from unlabelled log traces, which are then reused (and adapted) in the discovery of the outcome predictor. Results on reallife data confirmed that our proposal makes a more effective and robust solution for labelscarcity scenarios than current outcomeprediction methods.

Industrial text analytics for reliability with derivativefree optimization Abstract   Maintenance work order records provide valuable insights into chemical plants and production efficiency. These records are manually created in computerized management systems for routine and emergency maintenance. However, since the records are manually created, recording errors are not uncommon. The resulting datasets are additionally imbalanced, i.e., they have significantly more instances of certain classes than other minority classes. It is very challenging to use such datasets for classification and prediction of future events. In this paper, we propose a modeling framework that uses derivativefree optimization (DFO) to optimize the performance of classification models based on datasets that may be imbalanced. We apply our modeling framework to 15 realworld work order datasets. We also evaluate ten mixedinteger boxbounded DFO solvers for their ability to optimize machine learning models from industrial datasets. Compared to standard solutions, our results show dramatic improvements in the prediction accuracies of the models.