2IMC98 Kick-off meeting EIT_EDS
In this meeting the student gets insight into the study program. Staff
Education
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 …
When Portfolio Management meets Process Mining Challenges and Opportunities
FLIGHTMAP is Bicore’s flagship software solution for portfolio management. Since its launch in 2010, a growing group of international clients, such as DAF, Océ, and Fokker, have implemented FLIGHTMAP. With this tooling, they can perform roadmapping, budget and resource planning, scenario analysis, planning and tracking, and more. More information about FLIGHTMAP is available via www.flightmap.com. Read More …
Data Science: Developing a Self-Standing Dynamic reporting tool
A huge amount of (transaction) data is generated on a daily basis in ASML Development and Engineering department. The data is scattered in different sources. The challenge would be extracting data from relevant sources and creating a self-standing dynamic reporting tool (dashboard) demonstrating performance of (Supplier Quality) Engineers in different granularity levels (Department, Section, Individual) Read More …
Real-Time Model Discovery of the Service Order Process Using Stream Process Mining
Kropman Installatietechniek is a Dutch company established in 1934 and has become one of the leading companies of the Dutch installation industry. With about 800 employees, 12 regional locations and an annual turnover of more than 100 million Euro, Kropman is an integral service provider with a multidisciplinary approach. Kropman is mainly active in office Read More …
Print media identification based on sensor data (with Océ)
Océ is a Netherlands-based company that develops, manufactures and sells printing and copying hardware and related software. Their cut-sheet production printers are used to produce millions of prints on a daily basis. Print jobs may involve printing on different paper stock, which is loaded in the various paper trays of the machine. Print operators are Read More …
Process Oriented Query Language
Looking for a master project? Are you interested in topics like Process Mining, Process Querying and Data extraction?. Take a look at our proposals below. If you are interested, do not hesitate to contact Eduardo González or Hajo Reijers Generation of business reports by means of a process oriented query language Nowadays, most reports are Read More …
Do CHANGE project with Onmi Design
Onmi Design is currently working on the Do CHANGE project, funded in the Horizon 2020 program from the European Commission. In this project they are developing a health ecosystem that provides real-time behaviour change coaching based on input from various sensors. The student would assist in the development of analysis algorithms and integration of these 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 …
HA700 Honors part 1 track Big Data
Big Data In today’s connected world, data is everywhere and everybody talks about the analysis of “big data”. But what is this big data exactly and what can you do with it? In this track, you will learn about the state-of-the-art in data analysis through a number of company visits and literature. You will experience that Read More …
2IMC99 Kick-off meeting BDMA
In this meeting the student gets insight into the study program. Staff
2IMI00 Seminar Process Analytics
In this seminar, a group of master students will get in touch with research in the area of Information Systems, where Process Mining and Process Analysis from Event Data are the central themes. We study recent publications in the area of process mining and practical applications on real-life examples, to provide a good insight into Read More …
2IMI15 Metamodelling and Interoperability
Independently developed applications based on different models and implemented on different platforms need to use each others services and share each others data. Interoperability is therefore one of the buzz-words of the last years in Computer Science. Web services-driven Service-Oriented Architectures (SOA) have arisen as a solution to the interoperability problem. In this context, metamodeling Read More …
2IMI05 Capita selecta process analytics
People interested in the ‘process side’ of information systems can take the course ‘Capita selecta architecture of information systems’. This course will be organized in an ad-hoc manner taking into account the interests of the student. The focus will always be on a particular ‘hot topic’ in the information systems domain. The course can, in Read More …
2IMI10 Business Process Management Systems
This course focuses on enterprise information systems that are driven by models, i.e., instead of constructing code these systems are assembled, configured or generated using a model-driven approach. Of particular interest are so-called “process-aware” information systems. Typical examples are workflow management systems and the process engines of ERP, CRM, PDM and other enterprise information systems. Read More …