Activity Prediction of Business Process Instances using Deep Learning Techniques

The ability to predict the next activity of an ongoing case is becoming increasingly important in today’s businesses. Processes need to be monitored in real-life time in order to predict the remaining time of an open case, or also to be able to detect and prevent anomalies before they have a chance to impact the performances. Moreover, financial regulations and laws are changing, requiring companies' processes to be increasingly transparent. Process mining, supported by deep learning techniques, can improve the results of internal audit activities. The task of predicting the next activity can be used in this context to point out traces at risk that need to be monitored. In this way, the business is aware of the situation and, if possible, can take resolution actions in time. In recent years, this problem has been tackled using deep learning techniques, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) neural networks, achieving consistent results. The first contribution of this thesis consists of a generation of a real-life process mining dataset based on the Purchase-to-Pay (P2P) process. The SAP tables structure is taken into account since it is the most popular management software in today's companies. We exploit the simulated dataset to explore modeling techniques and to define the type and the quantity of anomalies. The second contribution of the thesis is an investigation of LSTM neural networks architectures that exploit information from both temporal data and static features, applied to the previously generated dataset. The neural networks are then used to predict future events characteristics of running traces. Finally, real-life application of the results are discussed and future work proposals are presented.The ability to predict the next activity of an ongoing case is becoming increasingly important in today’s businesses. Processes need to be monitored in real-life time in order to predict the remaining time of an open case, or also to be able to detect and prevent anomalies before they have a chance to impact the performances. Moreover, financial regulations and laws are changing, requiring companies' processes to be increasingly transparent. Process mining, supported by deep learning techniques, can improve the results of internal audit activities. The task of predicting the next activity can be used in this context to point out traces at risk that need to be monitored. In this way, the business is aware of the situation and, if possible, can take resolution actions in time. In recent years, this problem has been tackled using deep learning techniques, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) neural networks, achieving consistent results. The first contribution of this thesis consists of a generation of a real-life process mining dataset based on the Purchase-to-Pay (P2P) process. The SAP tables structure is taken into account since it is the most popular management software in today's companies. We exploit the simulated dataset to explore modeling techniques and to define the type and the quantity of anomalies. The second contribution of the thesis is an investigation of LSTM neural networks architectures that exploit information from both temporal data and static features, applied to the previously generated dataset. The neural networks are then used to predict future events characteristics of running traces. Finally, real-life application of the results are discussed and future work proposals are presented

LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances

Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep Learning (IEEE DL'17) @ SSC

COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks

For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. Two machine learning and natural language processing techniques are demonstrated: one relying on feature engineering (COTA v1) and the other exploiting raw signals through deep learning architectures (COTA v2). COTA v1 employs a new approach that converts the multi-classification task into a ranking problem, demonstrating significantly better performance in the case of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a novel deep learning architecture that allows for heterogeneous input and output feature types and injection of prior knowledge through network architecture choices. This paper compares these models and their variants on the task of ticket classification and answer selection, showing model COTA v2 outperforms COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B test is conducted in a production setting validating the real-world impact of COTA in reducing issue resolution time by 10 percent without reducing customer satisfaction