Closing the process mining circle with CPN IDE

CPN Tools is a tool that is well-known in the Petri net community. CPN Tools provides a mature environment for constructing, simulating, and performing analysis of CPN (Coloured Petri Net) models. CPN Tools consists of an ML-based CPN simulator (the back-end), and a CPN editor (the front-end) that has been developed in the BETA programming Read More …

Predicting the need of Nursing or Care Home and Home Care

(Verpleeg- en Verzorgingshuizen en Thuiszorg (VVT) in Dutch)  The St. Antonius hospital in Nieuwegein is an ambitious top clinical training hospital. Every year, more than 40,000 patients are admitted, of which 6,000 patients go home through Care Mediation with home care or to a nursing or care home. This outflow is very erratic with seasonal influences. At Read More …

Explainable Industrial Optimizers

DELMIA Quintiq develops widely used optimizers providing high-quality solutions across a breadth of industries. Human planners, as users of the optimizers, possess substantial soft knowledge, which is often difficult or expensive to capture in mathematical optimization algorithms. The aim of the Explainable Industrial Optimizers project is to promote cooperative planning by the human users and Read More …

Run 13 of “Introduction to Process Mining with ProM” MOOC started on February 1, 2021

On February 1, 2021, the thirteenth run of the free FutureLearn online course ‘Introduction to process mining with ProM’ will start. Join the 17.000 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 …

Run 7 of “Process Mining in Healthcare” MOOC started on February 1, 2021

On February 1, 2021, the seventh 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 more than 3500 students registered for the first six runs. Healthcare in particular has come under increasing pressure to reduce cost while Read More …

New master: Data Science and Artificial Intelligence (in Dutch)

Data als brandstof voor kunstmatige intelligentie; TU/e start nieuwe masteropleiding EINDHOVEN – Zonder brandstof komt een auto niet vooruit. Hetzelfde principe gaat op voor kunstmatige intelligentie: zonder voldoende en goede data is daar niets intelligents aan. Een nieuwe master van de TU/e combineert daarom die twee disciplines. Bron: ED

Knowledge Graphs for Improving Robot Operations in Logistics

Summary In the context of the “Process Mining in Logistics” research project between Vanderlande Industries and TU Eindhoven, we are offering multiple Master projects on process mining on event data of large-scale material handling systems. The fundamental challenges addressed are size and volume (logistics process data is significantly larger than business processes) and integration of Read More …

Process Mining and Process Prediction in Logistics (Vanderlande)

Summary In the context of the “Process Mining in Logistics” research project between Vanderlande Industries, we are offering multiple Master projects on process mining on event data of large-scale material handling systems. The fundamental challenges addressed are size (logistics processes are a factor 10-100 larger than business processes), reliable performance analysis and process prediction. We Read More …

Workflow mining bij GGZNHN (in Dutch)

GGZNHN is een specialistische ggz-instelling die behandelingen biedt aan mensen met ernstige psychiatrische aandoeningen[1]. Deze opdracht heeft betrekking op de groep mensen (volwassenen) die wordt aangemeld door de huisarts voor een ambulante ggz-behandeling.  Cliënten worden rechtstreeks via de huisarts aangemeld, via externe ketenpartners of via de aan GGZNHN verbonden basis-ggz (Amici).  Een ambulante behandeling wordt Read More …

Online Spatial Prediction Model for Citizens’ Public Space Complaints in Eindhoven

  Smart cities approach does not only emphasize the implementations of new technologies in a city but also highlights the importance of using new technologies for enabling citizens’ engagement in urban planning processes. In that regard, ICTs play a vital role in (i) supporting citizens to report their complaints related to the public spaces (i.e. Read More …

Develop a Behavioral Event Data Query Language

Query languages are essential for exploring, working with data and directly answering questions from data. SQL is the prime example for answering questions on relational data. Behavioral data is recorded in the form of events with timestamps. Various techniques such as Process Mining use the data in the form of event logs to aggregate and Read More …

Process Mining and Process Prediction in Logistics (Vanderlande)

Summary In the context of the “Process Mining in Logistics” research project between Vanderlande Industries, we are offering multiple Master projects on process mining on event data of large-scale material handling systems. The fundamental challenges addressed are size (logistics processes are a factor 10-100 larger than business processes), reliable performance analysis and process prediction. We Read More …

2AMI10 Foundations of Process Mining

Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based Read More …

2AMI20 Advanced Process Mining

Understanding and predicting behavior of people and machines in a shared setting (task, project, factory, process, organization) is central to Data Science and Artificial Intelligence. Actions of people and machines can be recorded as discrete events in event sequences (logs), event databases (tables, graphs), and real-time event streams. Learning behavioral models of discrete event data 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 …

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 …

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 …

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 …