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 …

Run 6 of “Process Mining in Healthcare” MOOC starts on April 13th, 2020

On April 13, 2020, the sixth run of the free FutureLearn online course ‘Process mining in healthcare’ will start, register now! We are happy to be able to run this course again, after over 2,500 students registered for the first three runs. Healthcare in particular has come under increasing pressure to reduce cost while improving Read More …

Run 12 of “Introduction to Process Mining with ProM” MOOC starts at April 13, 2020

On April 13, 2020, the twelfth run of the free FutureLearn online course ‘Introduction to process mining with ProM’ will start. Join the 15.000 students who enrolled before you and join the course! Process mining is a novel collection of techniques that connects the areas of data science and business process management. Using process mining Read More …

Mercedes-Benz Customer Assistance Center in Maastricht

Mercedes-Benz are well recognized as industry leaders in luxury service and high quality products, pushing the meaning of automobile excellence to new boundaries. You can trace the timeline of Mercedes-Benz all the way back to 1885 when Karl Benz invented the first automobile – cementing Mercedes-Benz a place in history. We know what you’re thinking, Read More …

Emergency and Operating rooms at HagaZiekenhuis

Two of the most important parts of a hospital are the Emergency room and the Operating room. HagaZiekenhuis deals with tens of thousands of patients every year. For each patient, everything is recorded in the Electronic Patient Record (Chipsoft Hix). Exploring the data recorded by both areas of the hospital opens the possibilities to understand Read More …

Happy nurses

The biggest problem that Dutch hospitals face today is the lack of nursing staff. Therefore, hospital beds are closed to maintain safety levels for the already admitted patients. However, patients on the waiting list or those already planned for elective care experience longer waiting times. And sometimes emergency care beds have to be closed causing 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 …

2IAB0 Data analytics for engineers

Learning goals Students gain insight in basic techniques for processing large amounts of data in an efficient, reliable, and consistent way. Students develop skills in understanding, interpreting, and documenting data and information in the context of realistic scenarios. Students get understanding of the data life cycle and develop skills for structuring their solutions of practical Read More …

2IIH0 Process Modelling and Simulation

Processes are everywhere in organizations and modern life is often governed by all kinds of processes, ranging from administrative processes to handle admission to a university to logistic processes to handle packages being delivered to customers who ordered online. Especially in administrative processes, concurrency plays an important role as multiple things can happen in parallel Read More …

Process Discovery using Generative Adversarial Neural Networks

Process Discovery is an unsupervised learning problem with the task of discovering a graph-based model from sequences (or graphs) of event data that describes the data best. Generative Adversarial Neural Networks (GANNs) are a type of neural networks used to learn structures in an unsupervised fashion. The objective of this project is to explore the Read More …

Process Mining on Event Graph Databases (multiple projects)

Process mining assumes event data to be stored in an event log, which is technically either a relational table (attributes as columns) or a stream of events (attribute value pairs). Recently, we developed a new technique to store event data in a Graph database such as Neo4j. This allows to do process mining over various Read More …

Mining processes, social networks, and queues (multiple projects)

A recent visual analytics technique called the “Performance Spectrum” https://github.com/processmining-in-logistics/psm allows us to gain more fine-grained insights into performance behavior and changes over time. A TU/e Master student showed that it is possible to mine synchronization of cases from the performance spectrum data showing that the behavior of a case depends on the mechanisms and Read More …

Process Mining with Textual Data

In many application domains, a process execution is captured using natural language. Think of medical records, customer complaints, legal records… The same holds for process models: they can be captured as text for medical guidelines, user manuals, legal regulations are typical examples of such cases. Such data forms a new challenge for the process mining Read More …

Real-Time Process Mining for Customer Journey Data

Available process discovery have been tested in the customer journey context under offline settings. Recent online process discovery approaches like: https://ieeexplore.ieee.org/document/7376771 bring however a lot of added value for a real-time customer journey optimization. The objective of this assignment is to use two different customer journey datasets to test the effectiveness of such approaches for Read More …

Finding Patterns in Evolving Graphs

The analysis of the temporal evolution of dynamic graphs like social networks is a key challenge for understanding complex processes hidden in graph structured data. Graph evolution rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing rule discovery methods make restrictive assumptions on the Read More …

Using Sequential Pattern Mining to Detect Drifts in Streaming Data

BFSPMiner is an effective and efficient batch-free algorithm for mining sequential patterns over data streams was published very recently https://link.springer.com/article/10.1007/s41060-017-0084-8. An implementation of the algorithm is available here: https://github.com/Xsea/BFSPMiner. As BFSPMiner has proven to be effective (see Figures 10-14 of the paper) in different domains (see Table 1 in the paper), we would like to Read More …

Efficient unsupervised event context detection

for event log clustering, outlier detection, and pre-processing. We recently developed a technique to detect the context of events from an event log in an efficient way through sub-graph matching. This allows to identify events and parts of event logs which are similar or different to each other, allowing to cluster traces, detect outliers, and Read More …

Smart event log pre-processing

The quality of process mining results highly depends on the quality of the input data where noise, infrequent behaviors, log incompleteness or many different variants undercut the assumptions of process discovery algorithms, and lead to low-quality results. ProM provides numerous event log pre-processing and filtering options, but they require expert knowledge to understand when which Read More …

Log Data Anonymization

In the context of process mining, we are often confronted with companies willing to share their data if we can sufficiently anonymize this. However, to date, there are no well-defined plugins to do such anonymizations. Therefore, we are looking for a Master student that is willing to help us with this. Part of the project Read More …

Adding heuristics to the Block Layout

The Block Layout can be used to create a layout for a process graph. For this, it uses well-known Petri-net-based reduction rules to reduce the entire net into a single place. For nicely structured process graphs, this layout works quite well, but for more complex structured graphs, the resulting layout needs to be improved. Either Read More …

N-out-of-M patterns in alignments

Aligning structured process models to event logs is a far from trivial task. In complex modelling languages, inclusive OR-split/join patterns play an important role and they are known to be notoriously difficult to align to event logs due to their large state-spaces. The known Petri net translations of OR-joins rely either on token coloring or Read More …

Generating non block-structured models and corresponding logs

For experimenting with process discovery and Petri nets, scientists often rely on experiments with artificial models and logs. More often than not, these models are block structured as it is easy to generate such models by simply building a random process tree and translating that into a Petri net. However, Petri nets allow for more Read More …

Petri net reduction rules for replay

Replaying event logs on Petri nets, either through token-replay or using alignments, is a complex task. Especially when models become larger and have more labels, the size of the models becomes a problem. In Petri net theory, many reduction rules exist for reducing Petri nets while retaining, for example, soundness of the model. Can we Read More …

Resolving kpn Customer Journey Variances through a Suitable Similarity Measure

The customer journey approach is quickly becoming the game changer within KPN  to become a customer-centric service provider and to improve the customer experience to an un-telco like level. Within this context, we have already connected various touch points of the customer, including calls, chats, store visits, online visits and engineer visits, and we are Read More …

Enriching Customer Journey Prediction in kpn with Context Data

The customer journey approach is quickly becoming the game changer within KPN to become a customer-centric service provider and to improve the customer experience to an un-telco like level. Within this context, we have already connected various touch points of the customer, including calls, chats, store visits, online visits and engineer visits, and we are Read More …

Data-driven Product Design at Philips Healthcare (3 Master projects)

The study of user behaviour is an important part of the product design process. This process is particularly more difficult when dealing with products that require very complex user interaction. Therefore, obtaining as much product usage information as possible is needed, since it can reveal patterns in the user behaviour that indicate a misalignment between Read More …

Automatically Matching Requirements to Process Models Determining the Impact of Change (ACCHA)

Target audience: Computer Science students with a data science/machine learning/NLP background. Task description: The main task of this Master thesis is to develop and implement a technique that is able to automatically link textual requirements to model-based representations of business process (so-called process models). By doing so, it will be possible to quantify the impact Read More …

Run 10 of “Introduction to process mining with ProM” MOOC starts on April 1st, 2019

On April 1, 2019, the tenth run of the free FutureLearn online course ‘Introduction to process mining with ProM’ will start. Join the 13.000 students who enrolled before you and join the course now! Process mining is a novel collection of techniques that connects the areas of data science and business process management. Using process Read More …

Run 4 of “Process Mining in Healthcare” MOOC starts on April 1st, 2019

On April 1, 2019, the fourth run of the free FutureLearn online course ‘Process mining in healthcare’ will start, register now! We are happy to be able to run this course again, after over 2,500 students registered for the first three runs. Healthcare in particular has come under increasing pressure to reduce cost while improving Read More …

Business Process Mining and Modeling at Amsterdam municipality (4 positions)

Amsterdam is a dynamic metropolis with great ambitions: Creating an excellent urban climate of living, working and leisure, but also a decisive and effective government. The city is a ‘living lab’, where metropolitan tasks are both a challenge and an opportunity to develop and apply new insights, technologies and practices. In this context, Amsterdam municipality Read More …

Using Process Mining to find lead indicators of Quality Issues

Using process mining in combination with (more regular) data analysis to predict the chance of part failure during EUV assembly and testing. Introduction ASML is a worldwide market leader in the production of lithography systems. These systems are used by customers like Intel and Samsung for their production of integrated circuits. Lithography systems are large Read More …

Detecting root causes of complaints and investigating the continuation within the customer journey

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 important Read More …

Advanced Process Mining techniques in Practice (several Master projects with ProcessGold)

ProcessGold is a software supplier bringing together Process Mining and Business Intelligence, driven by highly skilled ICT entrepreneurs and backed by a wealth of experience. ProcessGold recently released a new Process Mining platform, the ProcessGold Enterprise Platform, that combines data extraction, process mining techniques, and visual analytics in order to produce dynamic visual reports which Read More …

Philips HUE Product Evolution Using Stream Mining of Customer Journey

Philips HUE is a connected personal lighting system. It is controlled by a range of apps and smart home devices. To acquire Philips HUE, one starts with a starter kit that consists of a few lamps and a bridge. Subsequently, consumers decide to expand their system with additional lamps or/and physical sensors. About 50 lamps Read More …

Process mining in Logistics – 3D Visualization and Scalable Process Mining on Big Event Data (2 Topics)

Vanderlande is the global market leader for value-added logistic process automation at airports and in the parcel market. The company is also a leading supplier of process automation solutions for warehouses. Some figures: Vanderlande’s baggage handling systems move 3.7 billion pieces of luggage around the world per year. Our systems are active in 600 airports Read More …