PhD position on Model Adaptability for Privacy-Aware Process Mining (V32.3349)

PhD position on Model Adaptability for Privacy-Aware Process Mining

In the context of the EU H2020 project BPR4GDPR (Business Process Re-engineering and functional toolkit for GDPR compliance), a PhD position is open at the Analytics for Information System (AIS) group (www.win.tue.nl/ais/) in TU/e¿s Department for Mathematics and Computer Science in the domain of Model Adaptability.
Aanstellingsfunctie(s)
Promovendus
Faculteit(en)
Faculteit Wiskunde & Informatica
Instituten en overigen
Data Science Center Eindhoven
FTE
1,0
Sluitdatum
31/07/2018
Aanvraagnummer
V32.3349

Functieomschrijving

The broader scope of the BPR4GDPR project

In the last two decades the focus on process-orientation (e.g., process-aware information systems or BPM systems) has increased, while, with the incredible growth of event data (cf. “Big Data”), it has become possible to use process mining, i.e., a posteriori analysis technique exploiting the information recorded in event logs, to discover models and check the conformance of existing ones. Indeed, most organisations have very limited knowledge about the reality happening throughout their day-to-day operation; process mining focuses on this kind of problem, with a view to assessing the organisational reality and reduce the gap between what is supposed to happen and what actually happens. The key facets of process mining are discovery, monitoring and improvement of real processes by extracting knowledge from the organisation’s available data. Previous research has pointed large discrepancies between the idealized model and the process in reality. Moreover, process mining has shown that different models are possible for different and particular views on the process at hand.

The goal of BPR4GDPR is to support the implementation of a Privacy-Aware Process Mining Framework, seeking to meet requirements related to: transparency, being able to discover and integrate interpretable business procedures into a process model, i.e., to generate process models reflecting, as precisely as possible, an organisation’s current modus operandi; compliance, automatically identifying “business rules” for different perspectives; and accountability, spotting non-conformant executions. While checking the conformance between a process model and events in reality, two main concepts should be considered: real-time data and concept drift.

Additionally, process modelling and adaptability techniques will be implemented within the framework of this project to support concept drift, i.e. sensitive changes for businesses to fulfil the new requirements of GDPR (General Data Protection Regulation).

Privacy-aware business modelling

For the position, the PhD candidate is expected to work on model adaptability and will be supervised by Dr. Renata Medeiros de Carvalho and Dr. Boudewijn van Dongen. Considering non-stationary domains, business rules may become less accurate over time (a concept drift problem) or new factors/requirements may arise, so that the process model will be out-of-date and in need to be adapted/improved.

Furthermore, to respect the requirements of EU’s GDPR (put into effect from the end of May 2018), businesses are not allowed to store user sensitive data unless clearly authorized by end users. Even in cases when an authorization is obtained, users will always keep the right of their profile data “to be forgotten”.

Position

In light of the above, both active and passive solutions should be provided. The former type should define a change-detection system that updates the statistics about the data-related behaviours and establishes rules to integrate recent information to improve the model. The latter should offer continuous update, frequently retraining the model based on the most recent observations. The reveal of such adaptations should be supported by the data recorded from previous executions of the business processes, as well as by the ongoing executions generating data at real-time.

The Analytics for Information Systems (AIS) group provides its long running expertise and experience across all challenges of BPM, process modelling and process mining. In addition, BPR4GDPR the project has one more PhD position on streaming process mining that will closely collaborate on the project and solve the upcoming challenges jointly from different angles.

Functie-eisen

We are looking for a candidate that meets the following requirements:

  • a solid background in Computer Science, Data Science, or Business Information Systems (demonstrated by a relevant Master);
  • has a strong background in at least one of the following domains: BPM, model-driven engineering, evolution of models, model transformation, data mining, process mining;
  • has a strong interest in process models and data science research;
  • has the ability to realize research ideas in terms of prototype software, so software development skills are needed.
  • is highly motivated, rigorous, and disciplined when developing algorithms and software according to high quality standards;
  • good communication skills in English, both in speaking and in writing (candidates from non-Dutch or non-English speaking countries should be prepared to prove their English language skills);
  • possesses good communication capabilities and be an efficient team worker.

A PhD candidate is expected to:

  • perform scientific research in the domain described
  • collaborate with other researchers in this project
  • present results at (international) conferences
  • publish results in scientific journals
  • participate in activities of the group and department
  • assist in teaching undergraduate/graduate courses
  • participate in doctoral training on relevant topics

Arbeidsvoorwaarden

We offer:

  • A full-time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months;
  • A gross salary of € (2.222) per month in the first year increasing up to  € (2.840) in the fourth year;
  • Strong collaboration ties with several research groups in Europe and world-wide
  • Healthy travel funding for presenting your work at the leading conferences, for project meetings with multiple European partners and for visiting research.
  • Support for your personal development and career planning including courses, summer schools, conference visits etc.;
  • broad package of fringe benefits (e.g. excellent technical infrastructure, child daycare, excellent sports facilities, extra holiday allowance [8%, May], and end-of-year bonus [8.3%, December]).

Informatie en sollicitatie

More information

  • For more information about this position contact Dr. Renata Medeiros de Carvalho (Assistant Professor), e-mail: r.carvalho@tue.nl or by telephone: +31 40 247 4144 (https://www.win.tue.nl/~rmedeiro/)
  • For more information about the employment conditions contact the department HR advisor, e-mail: pzwin@tue.nl

Application

The application should consist of the following parts:

  • Cover letter explaining your motivation and qualifications for the position (the letter should also show an understanding of process mining and the work done within AIS, see websites such as www.processmining.org and the book "Process Mining: Discovery, Conformance and Enhancement of Business Processes");
  • Detailed Curriculum Vitae;
  • A copy or a link to your MSc thesis. If you have not completed it yet, please explain your current situation;
  • List of courses taken at the Bachelor and Master level including marks;
  • List of publications and software artefacts developed (if applicable);
  • Names of at least three referees.

Please note that a maximum of 5 documents of each 2 MB each can be uploaded. If you have more than 5 documents you will have to combine them. Incomplete applications will not be considered.

The selection process will start in June 2018 and will continue until the position gets filled. The position is fully funded and immediately available. The successful candidates are expected to start ASAP.

You can apply via the 'Apply now' button on top of this page. Applications via e-mail will not be accepted.