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Students

Master Projects

Are you looking for a Master’s thesis topic that

  • solves a challenging real-world problem,
  • allows you to become a specialist in the area of processes and information systems,
  • gives you cutting-edge knowledge to solve today’s problems regarding processes and their use in all kinds of organizations and companies?

The AIS group offers you thesis topics and supervision in a group

  • lead by one of the most cited and influential researchers in the area of business processes and process-aware information systems worldwide, Wil van der Aalst,
  • that has invented and developed cutting edge technologies that were applied in over 100 different companies and is now being used in various industrial tools,
  • that has set and contributed to various standards in the area of process-aware information systems,
  • that actively cooperates with many large companies in the area of process-aware information systems in the Netherlands and worldwide, as well as with universities and researchers from all over the world (we are currently running 17 projects with different partners),
  • that gives you clear and regular guidance through the challenges of a Master’s thesis project with the freedom to develop your own ideas, and
  • that after all is fun and supportive to work with.

Possible assignments

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.

→ Read more...

Improving the Evolutionary Tree Miner

The Evolutionary Tree Miner (ETM) is a genetic process discovery algorithm that works on process trees, a specific process modelling formalism. Recently work has started on an interactive process discovery algorithm where the user is guided to modify a free-choice Petri net in such a way that the Petri net is always sound.

The master project would consist initially of ‘connecting’ the ETM to this interactive process discovery approach, hence replacing the human. The main benefit would be that the ETM does not solely work on process trees any more (which can be somewhat restrictive), but directly on Petri nets in such a way that they are guaranteed to remain sound.

After this initial step several other improvement steps can be applied to the ETM such as extending the work in deriving alignments, estimating quality of a process model, smarter mutations, smarter application of mutations, starting from solutions created by other algorithms, etc. etc.

Therefore an important part is the implementation of these ideas in the ETM which is implemented in our toolset ProM. Good programming skills in Java are therefore important, no matter if you are a BIS or a CSE student.

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SAP Process Mining at Ciber

Ciber Netherlands [1] is an IT consulting company, with its origins in Detroit, integrated now within the Manpower group. They have a strong interest in using Process Mining to improve their systems and the services they provide to their customers. In particular, they wish to apply Process Mining to the systems they use for managing their internal processes. Their interest is on focusing their efforts on the SAP platform, which many of their clients use as well. Performing this project on their own SAP systems would be a great way to demonstrate the potential of Process Mining in these kind of environments, which they would be able to extend for their clients. Until now, Process Mining on SAP systems has been performed in an ad-hoc fashion. However, we aim at automating this procedure and applying new techniques [2,3] to extract the relevant information. These techniques allow to retrieve historical and execution information, enabling the application of analysis methods in a more standardized and meaningful way. To apply these techniques in real-life scenarios, many challenges remain open. One of them is to be able to identify interesting views on the data in order to obtain useful representations of the process. Often, this requires the involvement of domain experts, but a more automatic way would be desirable. Therefore, the project would not only involve the application of existing techniques, but the development of new methods to identify meaningful views on the real data.

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Certification for the XES Standard

On November 11th, 2016, the IEEE Standards Association has officially published the XES Standard as IEEE Std 1849TM-2016: IEEE Standard for eXtensible Event Stream (XES) for Achieving Interopability in Event Logs and Event Streams. The IEEE Task Force on Process Mining has been driving the standardization process for over six years, because the standard allows for the exchange of event data between different process mining tools.

Through the XES Standard, event data to be transported from the location where it was generated to the location where it can be stored and analyzed, without losing semantics. The XES Standard enforces that this transport and storage is done in a standardized way, that is, in a way that is clear and well-understood. Next to providing a standardized syntax and semantics, the XES Standard also allows for extensions, e.g., adding cost information or domain specific attributes to events.

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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 mainly busy with loading the printer with new paper and unloading the printed paper stacks. As this is a human task, errors are made in the process, especially in loading the paper trays with the correct paper stock.

Using the wrong media for a print job may lead to print quality issues and is most often unacceptable for the print buyer. For these reasons, a timely and automatic detection that the wrong media is loaded is needed to guarantee the overall production quality.

Your assignment is to use machine learning to determine whether the loaded paper media in a paper tray actually matches the medium specified on the printer. As input, a set of data can be used that is logged during the printer operation.

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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

→ 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 are easy to monitor and analyze for process stakeholders. These reports form the basis for deeper, fact-driven analysis and continuous process improvement projects.

In this context, ProcessGold is constantly offering graduation internships for investigating new techniques and methodologies in Process Mining and their application in a business context. A few example topics are given below - the specific graduation project and scope and will be further developed in mutual agreement.

→ Read more...

Operational Support for Analysis and Avoidance of Threats and Vulnerabilities in Global Supply-chain Processes (2 Master projects)

Supply-chain and Logistics processes are facing threats and issues as never before. Because of terrorisms and other forms of undesirable or illegal activities, supply chains are subjected to high vulnerabilities and disruptions. Also, the competition among the different supply-chain providers is requiring a timely and more efficient flow legitimate commerce through the European Union (EU) and other nations around the world. The aim of these Master projects is to demonstrate that vulnerabilities and inefficiencies can be at some degree predicted and recommendations can be given to minimize threats and risks, with tangible benefits to involved stakeholders. During the project to achieve the expected results, students will leverage on techniques that combine Process and Data Mining, such as classification based on decision/regression trees, OLAP technologies, process discovery and compliance checking. Specifically, two Master projects will be offered. A first project is in partnership with Jan De Rijk, a leading provider of transportation and distribution services, operating a large, modern and diversified fleet of 1000 vehicles across Europe. A second project is carried on in collaboration with Portbase, a community that brings together more than 3200 customers in all sectors of the Rotterdam port and provides integration services. In both of projects, students will be working on the datasets of the respective companies and will be given the opportunity to pay multiple visits to the companies and discuss with the different stakeholders

  • How to access and understand the historical event data
  • To discuss the business requirements
  • To present and obtain feedback on the (intermediate) results.

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Big data: finding, refining and monetizing – Making it possible for wheelchair users to travel with BookSpecials

Over the last 20 years the travel industry has changed radically. Through online information sharing, now the whole world lies at everybody’s feet. However, this applies not for the 50-75 million wheelchair users in the world. They are struggling a lot with organizing their day trips and vacations. As a result, they go much less often away, much less far away and many times to the same (dull) destinations. If they want something else, they literally need to do months of research beforehand.

The online travel sites possess huge amounts of data. The problem for wheelchair users is that nothing is categorized or searchable. For example, there are reviews from wheelchair users available on TripAdvisor, but it is impossible to search for them. And when there is data available, it is mostly wrong or incomplete. For example, about 10% of the vacation home rentals state that they are wheelchair accessible. In reality, probably only 2-5% of these actually are. This means that when a wheelchair user for example likes to rent an apartment via Airbnb, that this person probably needs to screen about 50 ‘wheelchair accessible’ listings to find only one that seems accessible indeed. And then, finding the right accommodation is only step one. Next is air travel, public transport, attractions, restaurants, etc.

This project assignment is about how to find, refine, combine, present and monetize all ‘publicly available’ travel information for wheelchair users.

“So that they can do a world trip by themselves as well.”

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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 algorithms in the system. For more information on the project please visit www.do-change.eu.

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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 will be an in-depth investigation in existing anonymization techniques. Which techniques are available and what analysis properties do they preserve. More importantly, how difficult is it to de-anonymize the data?

Another important part is the implementation of an anonymization framework in our toolset ProM. Good programming skills in Java are therefore important, no matter if you are a BIS or a CSE student.

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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:

Process Modeling/Analysis

While various types of process notations are used in industry, formal models such as Petri nets and temporal logics are more suitable for analysis purposes. Driven by questions from the other two research lines (Process Mining and PAIS Technology), particular models (e.g., WF-nets, WF-nets with data and resources, and declarative models) are used to answer questions related to correctness and performance. The main techniques that are used are model checking, structural techniques (e.g. invariants), and simulation.

Process Mining

Process mining techniques are used to extract process-related information from event logs, e.g., to automatically discover models, check conformance, and augment existing models with additional insights extracted from some event log. The main difference with Process Modeling/Analysis is that event logs play a central role (rather than predefined process models). One of the main challenges is to significantly improve the state-of-the-art in process discovery, e.g., we want to be able to deal with less structured processes and huge data sets (“Big Data”).

PAIS Technology

PAISs are used to manage and execute operational processes involving people, applications, and/or information sources. Examples are WFM (Workflow Management), BPM (Business Process Management), and ERP (Enterprise Resource Planning) systems. Increasingly, these systems are driven by models (connection to Process Modeling/Analysis) and produce high-quality event logs (connection to Process Mining). We are interested in the artifacts used and produced by these systems (i.e., models and logs) as these are essential for testing the techniques developed in the two other research lines.

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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Staff involved

Wil van der Aalst
Position: HGL
Room: MF 7.103
Tel (internal): 4295
Projects: 3TU.BSR, Core, CoseLoG, DeLiBiDa, DSC/e & NWO Graduate Program, Fluxicon, Phlips Flagship, Process Mining in Logistics, RISE BPM
Courses: 2IIE0, 2IMI05, 2IMI20, 2IMI35
Links: Personal home page, Google scholar page, TU/e employee page, DSC/e
Prof.dr.ir. Wil van der Aalst is a full professor of Information Systems and chair of the AIS group. He is also the scientific director of the Data Science Center Eindhoven (DSC/e). His personal research interests include process mining, business process management, workflow management, Petri nets, process modeling, and process analysis.
Joos Buijs
Position: UD
Room: MF 7.062
Tel (internal): 3661
Projects: CoseLoG, EDSA, Phlips Flagship, RISE BPM
Courses: 2IMI20
Links: Personal home page, Google scholar page, TU/e employee page, DSC/e
Joos Buijs' current research interests include Process mining in healthcare and Learning analytics. Next to these research topics Joos is also involved in MOOC creation. Related to the learning analytics of course, we also create MOOCs on the topic of process mining. There is the Coursera MOOC "Process Mining: Data Science in Action". And on July 11, 2016 the first session of the FutureLearn MOOC "Process mining with ProM" will launch, a hands-on MOOC where you learn how you can apply process mining on your own data!
Boudewijn van Dongen
Position: UD
Room: MF 7.064
Tel (internal): 2181
Projects: 3TU.BSR, CoseLoG, DeLiBiDa
Courses: 2IMC92, 2IMC97, 2IMI30
Links: Google scholar page, TU/e employee page
Boudewijn’s research focusses on conformance checking. Conformance checking is considered to be anything where observed behavior, needs to be related to already modeled behavior. Conformance checking is embedded in the larger contexts of Business Process Management and Process Mining. Boudewijn aims to develop techniques and tools to analyze databases and logs of large-scale information systems for the purpose of detecting, isolating, diagnosing and predicting misconformance in the business processes supported by these systems. The notion of alignments play a seminal role in conformance checking and the AIS group is world-leading in the definition of alignments for various types of observed behavior and for various modelling languages.
Massimiliano de Leoni
Position: UD
Room: MF 7.059
Tel (internal): 8430
Projects: Core
Courses: 2IHI10/2IIC0, 2IIE0, 2IMI35
Links: Personal home page, Google scholar page, TU/e employee page
Dr. Massimiliano de Leoni is assistant professor at the AIS group. His research focuses in the areas of Process-aware Information Systems and Business Process Management, predominantly on multi-perspective process mining, process-aware decision support systems as well as on visualization techniques for business process management and analysis. He has a genuine interest in the practical applications of his research in real-life settings, which led him to concretely develop his ideas in term of software tools and apply them with a large number of organizations world-wide.
Dirk Fahland
Position: UD
Room: MF 7.066
Tel (internal): 4804
Projects: BOSS, Process Mining in Logistics
Courses: 2IHI10/2IIC0, 2IMI10, 2IOC0, JBG030
Links: Personal home page, Google scholar page, TU/e employee page
Dirk is Assistant Professor (UD) in the AIS group. He completed his PhD with summa cum laude at Humboldt-Univeristät zu Berlin and Eindhoven University of Technology in 2010. His research interests include distributed processes and systems built from distributed components for which he investigates modeling systems (using process modeling languages, Petri nets, or scenario-based techniques), analyzing systems for errors or misconformances (through verification or simulation), and process mining/specification mining techniques for discovering system models from event logs. He particularly focuses on distributed system with multi-instance characteristics and their synchronizing and interacting behaviors. Dirk published his research results in over 40 articles at international conferences and journals and implemented them in a number of software tools.
Marwan Hassani
Position: UD
Room: MF 7.097A
Tel (internal): 3887
Projects:
Courses: 2IHI10/2IIC0
Links: Personal home page, Google scholar page, TU/e employee page
Dr. Marwan Hassani is assistant professor at the AIS group. His research interests include streaming process mining, stream data mining, sequential pattern mining of multiple streams, efficient anytime clustering of big data streams and exploration of evolving graph data. Marwan received his PhD (2015) from RWTH Aachen University. He coauthored more than 45 scientific publications and serves on several program committees.
Farideh Heidari
Position: UD
Room: MF 7.119
Tel (internal): 2257
Projects:
Courses: 2IMC93, 2IMC98
Links: Personal home page, Google scholar page, TU/e employee page
Farideh has a multi-disciplinary educational and professional background: math and physics, mechanical and industrial engineering, and PhD in information systems. Her unique blend of experiences in academic and industrial areas has made Farideh a person with an original point of view and gave Farideh a broad perspective to life and a goal to aim. This enables Farideh to bring industry and research together to provide organisations with effective solutions to their businesses.
Wim Nuijten
Position: HGL
Room: Flex
Tel (internal): 3877 (2733)
Projects: DAIPEX
Courses: 2IMI25
Links: TU/e employee page
Prof. Wim Nuijten is a part-time professor at the AIS group focusing on Intelligent Information Systems. He is an expert in the area of constraint programming and advanced planning and scheduling. Currently, he is working for Quintiq, a company leading in the area of advanced planning and scheduling. Before, he worked within IBM and ILOG.
Hajo Reijers
Position: HGL
Room: Flex
Tel (internal): 3629
Projects: CoseLoG
Courses: 2IMI00
Links: Personal home page, Google scholar page, TU/e employee page
Prof.dr.ir. Hajo Reijers is a part-time, full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). He is also a full professor in Business Informatics at VU University Amsterdam. He is also affiliated to the TiasNimbas Business School, where he is involved as one of the core lecturers in the Executive Master of Operations and Supply Chain Excellence (MOS) program. Hajo Reijers is one of the founders of the Business Process Management Forum, a Dutch platform for the development and exchange of knowledge between industry and academia. Hajo Reijers received a PhD degree in Computer Science (2002), an MSc in Computer Science (1994), and an MSc in Technology and Society (cum laude) (1994), all from TU/e. Hajo Reijers wrote his PhD thesis while he was a manager with Deloitte. Previously, he also worked for Bakkenist Management Consultants and Accenture. As a consultant, he has been involved in various reengineering projects and workflow system implementations, particularly for governmental agencies and organizations offering financial services. From 2012 to 2014, Hajo Reijers headed the BPM R&D group of Perceptive Software. The focus of Hajo Reijers' academic research is on business process redesign, workflow management, conceptual modeling, process mining, and simulation. On these topics, he published over 150 scientific papers, chapters in edited books, and articles in professional journals.
Natalia Sidorova
Position: UD
Room: MF 7.105
Tel (internal): 3705
Projects: Phlips Flagship
Courses: 2IIE0, 2IMI15, 2IMI35
Links: Personal home page, Google scholar page, TU/e employee page
Dr. Natalia Sidorova is assistant professor at the AIS group. She actively works on topics related to process modeling and verification. The application domains include business processes and distributed systems. She has published more than 70 conference and journal papers. She is active in the Health and Wellbeing Action Line of EIT ICT Labs, taking lead of projects towards the development of innovative services for disease prevention making use of modern sensor technologies together with mining, conformance analysis, prediction and recommendation techniques.

Example projects

Bram in 't Groen

VDSEIR - A graphical layer on top of the Octopus toolset

Description

In his work, Bram in 't Groen introduces a graphical representation for DSEIR (a language used in the Octopus toolset for designing embedded systems) called Visual DSEIR (VDSEIR). By using VDSEIR, users of the toolset can create specifications in DSEIR by means of creating graphical models, removing the need for those users to know how to program in the Octopus API. Bram in 't Groen provides a model transformation from VDSEIR to DSEIR that makes use of an intermediate generator model and a parser that is automatically generated from an annotated JavaCC grammar. The graphical representation for DSEIR consists of several perspectives and it contains a special form of syntactic sugar, namely hierarchy. It is possible to create hierarchical models in the graphical representation without having support for hierarchy in the original DSEIR language, because these hierarchical models can be translated into non-hierarchical DSEIR models. This way, additional expressiveness is created for the user, without modifying the underlying toolset.

Type

AIS / External / ESI

Borana Luka

Model merging in the context of configurable process models

Description

While the role of business process models in the operation of modern organizations becomes more and more prominent, configurable process models have recently emerged as an approach that can facilitate their reuse, thereby helping to reduce costs and effort. Configurable models incorporate the behavior of several model variants into one model, which can be configured and individualized as necessary. The creation of configurable models is a complicated process, and tool support for it is in its early steps. In her thesis, Borana Luka evaluates two existing approaches to process model merging which are supported by tools and test an approach to model merging based on the similarity between models. Borana’s work resulted in a paper presented in the 2011 International Workshop on Process Model Collections.

Type

AIS / Internal / CoSeLog project involving 10 municpalities

Links
Staff involved
Cosmina Cristina Niculae

Guided configuration of industry reference models

Description

Configurable process models are compact representations of process families, capturing both the similarities and differences of business processes and further allowing for the individualization of such processes in line with particular requirements. Such a representation of business processes can be adopted in the consultancy sector and especially in the ERP market, as ERP systems represent general solutions applicable for a range of industries and need further configuration before being implemented to particular organizations. Configurable process models can potentially bring several benefits when used in practice, such as faster delivery times in project implementations or standardization of business processes. Cosmina Niculae conducted her project within To-Increase B.V., a company that specializes in ERP implementations. She developed an approach to make configuration much easier, implemented it, and tested it on real-life cases within To-Increase.

Type

AIS / External / To-Increase

Links
Staff involved
Dennis Schunselaar

Configurable Declare

Description

Declarative languages are becoming more popular for modeling business processes with a high degree of variability. Unlike procedural languages, where the models define what is to be done, a declarative model specifies what behavior is not allowed, using constraints on process events. In his thesis, Dennis Schunselaar studies how to support configurability in such a declarative setting. He takes Declare as an example of a declarative process modeling language and introduces Configurable Declare. Configurability is achieved by using configuration options for event hiding and constraint omission. He illustrated our approach using a case study, based on process models of ten Dutch municipalities. A Configurable Declare model is constructed supporting the variations within these municipalities.

Type

AIS / Internal

Links
Staff involved
Erik Nooijen

Artifact-Centric Process Analysis, Process discovery in ERP systems

Description

In his thesis, Erik Nooijen developed an automated technique for discovering process models from enterprise resource planning (ERP) systems. In such systems, several different processes interact together to maintain the resources of a business, where all information about the business resources are stored in a very large relational database. The challenge for discovering processes from ERP system data, is to identify from arbitrary tables how many different processes exist in the system, to extract event data for each instance of each process in the system. Erik Nooijen identified a number of data mining techniques that can solve these challenges and integrated them in a software tool, so that he can automatically extract for a process of the ERP system an event log containing all events of that process. Then classical process discovery techniques allow to show the different process models of the system. Erik conducted his project within Sligro where he is actively using his software to improve the company's processes. The thesis resulted in a workshop paper presented at the International Conference on Business Process Management 2012 in Tallinn, Estonia.

Type

AIS / External / Sligro

Links
Staff involved
Irina-Maria Ailenei

Process mining tools: A comparative analysis

Description

In her thesis, Irina-Maria Ailenei proposes an evaluation framework that is used to assess the strengths and the weaknesses of process mining tools. She applied the framework in practice for evaluating four process mining systems: ARIS PPM, Flow, Futura Reflect, and ProcessAnalyzer. The framework is based on a collection of use cases. A use case consists of a typical application of process mining functionality in a practical situation. The set of use cases was collected based on the functionality available in ProM and was validated by conducting a series of semi-structured interviews with process mining users and by conducting a survey. The validated collection of use cases formed the base of her tool evaluation. The project was conducted within Fluxicon and was created based on a request from Siemens. The work also resulted in a paper presented at the BPI workshop in Clermont-Ferrand in 2011.

Type

AIS / External / Fluxicon

Links
Staff involved
Stefania Rusu

Discovery and analysis of field service engineer process using process mining

Description

In her thesis, Stefania Rusu applied process mining techniques using event logs from Philips Healthcare to get insights into the workflow of Field Service Engineers. In order to get insights into Philips’s Field Service Engineers Stefania created hierarchical models that abstract from the low levels at which events are stored to higher levels of activities, based on the role of the analyst. Performance analysis based on process mining techniques helped to identify bottlenecks and thereby generated ideas for improvement regarding the work of Field Service Engineers within Philips Healthcare. Stefania proposed a new approach to annotate process maps with performance information extracted from event logs. To support the approach and to apply in in practice, she implemented a new plug-in in the ProM framework.

Type

AIS / External / Philips Healthcare

Links
Staff involved

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.

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