2 dagen geleden - Universiteit Utrecht (UU) - Utrecht
Utrecht University's Faculty of Social and Behavioural Sciences is looking for an Assistant Professor Methodology and Statistics. Are you interested? Then plea…
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/eÂ¿s Department for Mathematics and Computer Science in the domain of Model Adaptability.
The broader scope of the BPR4GDPR projectIn 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 modellingFor 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'.PositionIn 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.
We are looking for a candidate that meets the following requirements:
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