Speaker
Description
Temporal events are inherent parts of every industrial, business or generalized processes. The operational characteristic of these often high complexity processes is nicely represented by the generated temporal events. However, extracting useful knowledge from these large datasets and the process of model building using the extracted knowledge is by no means an easy task. Therefore, in our presentation, we would like to highlight and emphasize the application possibilities of some often neglected approaches for the analysis of large temporal event datasets. We present challenging problems of different fields of interest including process safety and churn analysis and present how the toolbox of data and process mining and predictive modeling can be utilised following the integrated information concept of Industry 4.0.
In our presentation, first, the multi-temporal sequence-based representation of the event series is described and statistical metrics (frequency, probability, confidence, etc.) for the characterization of the datasets are introduced. Based on the presented simple statistical metrics, we present a Bayesian model for the prediction of the next occurring event in our process.
Realizing the complexity of the event sequences, the applicability of advanced machine learning techniques like deep learning is investigated for the detection of the root cause of past events and the prediction of future sequences. The problem of root cause detection is formulated as a classification problem assuming a known set of root causes in our process, while the task of sequence prediction is formulated as a sequence to sequence (seq2seq) learning problem. In addition to the description of a recurrent neural network model of Long Short-Term Memory (LSTM) units for the solution of the above-mentioned tasks, we present how the multivariate data analysis techniques can be applied to extract knowledge from recurrent neural network models.
Finally, we present how the task of churn analysis and sequence mining is connected and how the topic of event (sequence) analysis facilitates the prediction of customer churn.
The applicability of the aforementioned tools is presented through the analysis of various industry-motivated problems like alarm management and customer churn prediction. The alarm management is the effective handling of industrial process alarms, where the extraction of useful knowledge from historical process data is a high-priority problem, while the problem of churn analysis is present in quality test sequence optimization, or in the prediction of customers who are likely to discontinue the use of a service.
The tools for the analysis of the various datasets were implemented in Matlab/Python, while the deep learning neural networks were trained by Tensorflow/Keras. Therefore, in our presentation, we intend to share our application experiences regarding these interfaces.
