Data Preparation for Data Mining in Chemical Plants using Big Data Data preparation for data mining in industrial applications is a key success factor which requires considerable repeated efforts. Although the required activities need to be repeated in very similar fashion across many projects, details of their implementation differ and require both application understanding and experience. As a result, data preparation is done by data mining experts with a strong domain background and a good understanding of the characteristics of the data to be analyzed. Experts with these profiles usually have an engineering background and no strong expertise in distributed programming or big data technology. Unfortunately, the amount of data can be so large that distributed algorithms are required to allow for inspection of results and iteration of preparation steps. This contribution introduces an interactive data preparation workflow for signal data from chemical plants enabling domain experts without background in distributed computing and extensive programming experience to leverage the power of big data technologies.

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.