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Master Projects

Procedure for Master projects

  • Within the AIS group, we offer Master projects in several different areas and also on concrete topics. We then tailor the Master project assignments for each individual student, with that student’s skills in mind. To create an individual assignment, you have to find and commit to a supervisor who helps you in this process.
  • If you already know with which supervisor/on which topic you want to graduate, contact the potential supervisor of your choice directly, and as early as possible (at the end of the first year, after you have obtained 40 to 50 credits)
  • If you first want to get an overview on available projects and supervisors, please contact Dirk Fahland. He can direct you to a potential supervisor in your area of interest, and you then talk with the potential about possible Master projects.
  • At some point the student has to commit to a particular supervisor. Only then an actual assignment can be defined, either internal or external. In particular external assignments must be discussed and defined together with the supervisor to ensure that the defined assignment fits the student, the company, and the study program.

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Research Lines

Assignments for master projects typically fall in one of the main three research lines of AIS:

Processes

The research group distinguishes itself in the Information Systems discipline by its fundamental focus on modelling, understanding, analyzing, and improving processes. Essentially, every time two or more activities are performed to reach a certain goal, fundamental principles of processes apply. Processes take place on the level of individual actors, groups of actors, entire organizations, and networks of organizations. Processes, in one form or another, can be seen everywhere:

In traditional workflow management settings, the idea behind a process is that there is a single notion of a “case” flowing through a “process” according to a pre-defined route, i.e. a “process model”. However, the notion of processes and the principles behind them are much broader. In many situations, processes are not explicitly defined (there is no procedure for a customer clicking through a website) or are highly flexible (typically in knowledge intensive processes such as designing products or deciding about visa and immigration). Also, many processes have interactions with other processes: for example, there are many interlinked processes behind every patient in a hospital or behind the supply chain of any multi-national manufacturer. There is a growing concern within organizations, governments, and society as a whole that processes need to be governed properly and efficiently. The availability of large amounts of data on the one hand and a fundamental understanding of the process notion on the other shape the opportunity for researchers in our group to contribute to this societal challenge.

Background: Business Process Management

Within organizations, the management of processes has been an important topic since the introduction of conveyor belts in the early 1920’s. From 2000, business process management is the research field focusing on agility in organizations and continuous (business) process improvement through a BPM life-cycle of designing, modelling, execution, monitoring and optimizing processes.

Lately, there is growing attention in the field of business process management for the embedding of process analytics into (process aware) information systems, i.e. BPM provides the context in which our analytics are being developed.

Fundamental to the research group at the Eindhoven University of Technology is the choice for Petri nets as the language to precisely describe process dynamics also in complex settings at a foundational level. The choice for this language is what distinguishes our research group from research groups in more industrial engineering oriented information systems groups.

Process Analytics

One of the foundations of computer science today is data. The omnipresence of increasingly large volumes of data has become a key driver for many innovations and new research directions in computer science. Specifically in information systems, data - and the analytics developed on top of this data - have transformed the field from expert-driven to evidence-based, which in turn massively broadens the applicability of results to more and larger contexts. Many advanced process analysis tools and techniques exist today in over 25 commercial packages that were developed in the AIS group over the last 15 years.

The research in the our group continues to expand outward from a “classical” situation of data with clear case notions in the context of explicitly structured processes to a broad, multi-faceted field, where processes are less structured or consist of many interacting artifacts and where case notions in data become more fluid or are complex, multi-dimensional networks.

The figure above shows this research field of process analytics.

Impact and Societal Relevance

When selecting a Master project it is advisable to consider the track record of the research group supervising the project. Therefore, we briefly discuss the impact and societal relevant of AIS’s research.

The work of the AIS group is world renowned, especially in the fields of

  1. the modeling and analysis of workflow processes (cf. workflow nets and the seminal soundness notion),
  2. workflow patterns (the DAPD paper on the workflow patterns is the most cited paper in the BPM domain and the Workflow Patterns web site is the most visited web site on workflow management over the last decade), and
  3. process mining (e.g., we established an international process mining community).

The impact of our work is reflected by the many citations of the publications of the AIS group. For example, Wil van der Aalst is the highest ranked European computer scientist based on Google Scholar. His work has been cited more than 63,000 times and his Hirsch Index is 120. During the last evaluation of all computer science groups in the Netherlands, the AIS group got the highest marks possible: 5-5-5-5-5 (i.e., a perfect score). The workflow patterns have had a very positive effect on commercial WFM/BPM products. Today, the patterns are widely used to describe workflow functionality in a language/system-independent manner. In addition, the patterns are also highly visible. The Wokflow Patterns web site has been one of the most visited web sites in the field of BPM averaging more than 300 unique visitors per working day over the last decade. Several vendors changed their tools to support more patterns and some have provided wizards based on the patterns. For example, IBM recently added a wizard-like functionality to their WebSphere product inspired by the patterns. Also standardization efforts were influenced by the patterns, see for example BPMN.

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Documents

Possible assignments

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 including 13 of the world’s top 20.
  • More than 39 million parcels are sorted by its systems every day, which have been installed for the world’s leading parcel companies.
  • Many of the largest global e-commerce players and distribution firms have confidence in Vanderlande’s efficient and reliable solutions.

Vanderlande focuses on the optimization of its customers’ business processes and competitive positions. Through close cooperation, we strive for the improvement of our customers’ operational activities and the expansion of their logistical achievements.

For Vanderlande, it is critical that we have state-of-the-art techniques to analyze and optimize our customers’ logistics processes. Reasons are (a) the constant increasing size and complexity of our material handling solutions, (b) growing complexity of our software solutions, covering larger parts of our customers’ business processes, and © the demand for more advanced service offerings, covering logistics and business services. We believe that process mining is of high value for Vanderlande. Therefore, we work with the Eindhoven University of Technology on making process mining fit for analyzing logistics processes. In this context, we offer graduation projects on process mining and the application in our business.

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Want to win?

Win the Process Discovery Contest (PDC) 2018!

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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. The figure above shows a screenshot of the tool obtained after the portfolio analysis

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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) based on a set of pre-defined KPIs.

Are you a master student in Software Engineering (Data Science) with a passion on real-life data challenges? Then we are looking for you!

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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 buildings, health care and industry. It offers design, construction and maintenance in the field of facility installations. Kropman also has a separate business for process installations and cleanrooms: Kropman Contamination Control. The maintenance (services) is a fast growing business line. The order process is fully supported within an ERP environment. The service order process is not a trouble-free process: the process takes too long and flows more often than necessary. The company aims at increasing the throughput of the SO process and decreasing the amount of process deviations by applying process mining and data mining techniques.

To have a better overview of the process, Kropman is aiming at:

  • Exposing bottlenecks happening in the SO process
  • Detecting deviations from the supposed model in the real time they are happening using stream process mining techniques

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Understanding Customer Journeys with Process Mining

In today’s customer environments, where customers use many different contact channels to solve outstanding questions and do requests, it becomes increasingly difficult to follow customer behavior, optimize service levels and provide a memorable experience for customers. Especially when the contacts of the customer are not related. These journeys can be product specific (e.g. changing your telephone provider), customer specific (e.g. change known home address) or life event specific (e.g. starting a family).

Underlined works together with several companies like CZ, Aegon and SNS to build a generic framework in which all traceable, incoming and outgoing customer contacts (call, web, e-mail, chat, etc.) are brought together as a unique dataset. The dataset of companies is further enriched by Underlined with relevant analysis that can be linked to (unique) customer events.

Underlined has worked together with the TU/e (Bart Hompes and Joos Buijs from the Architecture of Information Systems group) and CZ (one of the largest Dutch health insurance companies) to develop customer journey mining algorithms. This research showed that it is possible to distinguish the different journeys per customer without any prior process knowledge, but additional research is needed to:

  1. Apply machine learning techniques to cluster customers with similar behavior. The customer journey can significantly vary depending on the customer characteristics. Therefore, try to build a single model for all customers will lead to not fully satisfactory results. Therefore, the application of machine learning or OLAP techniques (a.k.a. as process cubes) would be beneficial to split the customers into clusters each containing customers with similar characteristics.
  2. Research multi-dimensional similarity matrix. Current customer journey mining algorithms need to know which activities might be related to each other. Information on this is stored in a similarity matrix. Currently there is a working version for relatively simple datasets and processes. We would like to develop a next level similarity matrix for more complex data which contain multiple journeys and multiple customer segments.
  3. Predictive modelling of emotions in the journey. Customers make decisions in the customer journey based on their emotions. In current datasets there is sample feedback, which expresses the feedback of customers and their concerns regarding the service of a company. We would to research a predictive model that, out the basis of the past customer journeys, it can predict the intermediate and final emotions of the customers who are currently active in their journeys.

In the above-mentioned analysis, the student should try to leverage on every piece of available data. This includes logged data of the past customer behavior, for example call center data, online click trails, social media data and online and offline feedback. But also non-transactional data like product usage and customer segmentation variables.

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Erasmus Mundus Joint Master in the field of Big Data Management and Analytics

The department of Computer Science at Technische Universiteit Eindhoven (TU/e) in a consortium of five European Universities is awarded EU co-funding to run a Erasmus Mundus Join Master Degree (EMJMDs) in the field of Big Data Management and Analytics (BDMA).

The programme curriculum is jointly delivered by Université libre de Bruxelles (ULB), Belgium, Universitat Politècnica de Catalunya (UPC), Spain, Technische Universität Berlin (TUB), Germany, Technische Universiteit Eindhoven (TU/e), the Netherlands, and Université François Rabelais Tours (UFRT), France. Academic partners around the world and partners from leading industries in BI and BD, private R&D companies, excellence centres, service companies, start-up incubators, public research institutes, and public authorities will contribute to the programme by giving lectures, training students, providing software, course material, and internships or job placements. The EMJMD in “Big Data Management and Analytics” (BDMA) is designed to provide understanding, knowledge, and skills in the broad scope of fields underlying Business Intelligence (BI) and Big Data (BD). Its main objective is to train computer scientists who have an in-depth understanding of the stakes, challenges, and open issues of gathering and analysing large amounts of heterogeneous data for decision-making purposes. The programme will prepare the graduates to answer the professional challenges of our data-driven society through a strong connection with industry, but also to pursue their studies into doctorate programmes through a strong connection with research and innovation

BDMA is a 2-year (120 ECTS) programme. The first two semesters are devoted to fundamentals on BI and BD delivered by ULB and UPC. Then, all students participate to the European Business Intelligence and Big Data Summer School (eBISS). In the third semester, students chose one of the three specialisations delivered by TUB, TU/e, and UFRT. The fourth semester is dedicated to the master's thesis and can be carried out as an internship in industry or in a research laboratory in any full or associated partner. Eventually, all students attend the Final Event devoted to master's theses defences and the graduation ceremony. The tuition language is English. The program targets students with a Bachelor of Science (or a level equivalent to 180 ETCS) with major in Computer Science, as well as an English proficiency corresponding to level B2 of CEFR. The programme will deliver a joint degree to graduates following the mobility ULB, UPC, and UFRT, and three degrees from ULB, UPC, and the university of the specialisation (UFRT, TUB or TU/e) to graduates following the other mobilities.

More information can be found on the UFRT BDMA site, which will open soon.


If you have a master project item for this page, which may include possible master project assignments, on-going master projects, and completed master projects, please send it to Eric Verbeek.