Applying Process Mining to Predict Customer Behavior and Recommend Actions

Background In the Dutch health care system health care insurance is obligated for all residents. The government sets the basis package and insurers compete based on price and service. Customer service is therefore very important for every health insurance company; especially in the fast changing digital world. As a result customer satisfaction is the most Read More …

Real-Time Prediction of Traveler Flow within Digital Stations

In the last decennia the pressure on different types of mobility have severely increased in the Netherlands. Therefor the need for availability and reliability has increased. Siemens Mobility supplies solutions in the Netherlands that contribute to the accessibility and quality of life in this regard. With the help of different technologies, data is being unlocked Read More …

Improving Traffic Flow Prediction in Urban Areas by Incorporating a Real-Time Outlier Detection Model

In the last decennia the pressure on different types of mobility have severely increased in the Netherlands. Therefor the need for availability and reliability has increased. Siemens Mobility supplies solutions in this regard that contribute to everyday accessibility and quality of life. With the help of different technologies, data is being unlocked through which the operation Read More …

Differential-private Process Mining (Multiple Assignments)

Within the BPR4GDPR EU project, we are researching (among others) methods that enable a privacy-aware utilization of sensitive individual information. Several anonymization techniques are not enough to completely keep the process discovery completely privacy aware (e.g. the existence of rare diseases can still be revealed from an anonymized log file). Adding exactly “the correct amount” of Read More …

Log-based vs. Model-based Concept Drift Detection

StrProMCDD is a recently published work that detects concept drifts in event streams (see the figure below). StrProMCDD uses several model-based distance measures to detect these deviations using an adaptive window concept. In this assignment, we would like to compare the performance of this model-based approach with log-based stream clustering approaches that try to detect drifts in Read More …