Philips Flagship

Posted onAuthorEric VerbeekLeave a comment

Description

The Data Science Centre Eindhoven (DSC/e) is TU/e’s response to the growing volume and importance of data and the need for data & process scientists (http://www.tue.nl/dsce/). The DSC/e has recently started a long-term strategic cooperation with Philips Research Eindhoven on three topics: data science, health and lighting. As a first concrete action, 70 PhD students are being hired for these three topics using joint funding from the TU/e and Philips, of which 18 PhD students work on the data science topic. These students form together with researchers from the TU/e and Philips a strong research community working together on scientific and industrial challenges.

The following four PhD positions will be related to the topic of process mining:

  1. Product-centric Consumer Data Analytics: Product Usage Lifecycle Analysis [part of the Data Driven Value Proposition theme]. Digital components are being added to Philips lifestyle products. The data from these products as well as from Philips touch points must be combined to optimize user experience and maintain customer satisfaction. Process mining techniques will be used to analyze the usage of products over a longer period of time.
  2. Transforming Event Data into Predictive Models [part of the Healthcare Smart Maintenance theme]. Philips has strong leadership positions in healthcare imaging and patient monitoring systems. In the healthcare domain, reducing equipment downtime and cost of ownership for hospitals is of vital importance. Smart maintenance exploits that professional equipment is connected to the internet and aims to use event and sensor data for overall cost reduction. Process mining techniques will be used to learn dynamic models that can be used for prediction and optimization.
  3. Predictive Analytics for Healthcare Workflows [part of the Optimizing Healthcare Workflows theme]. Processes play an important role in pathology and radiology. It is not just about collecting data and supporting individual activities, but also about improving the underlying end-to-end workflow processes. To improve these operational processes in terms of costs, efficiency, speed, reliability, and conformance, we can learn from the way that processes are conducted in practice. One can learn from problems in the past and compare different process variants and process instances. This project aims to obtain insight in these workflows, in order to understand what goes well and what can be improved, using a process mining approach. The cross-fertilization between process mining and visualization will provide a novel angle on workflow improvements in pathology and radiology.
  4. Radiology Workflow Optimization and Orchestration [also part of the Optimizing Healthcare Workflows theme]. Radiology, involves complex workflows, especially when seen in its clinical context. This project aims to obtain insight in these workflows and their visualization, in order to understand what goes well and what can be improved, using a visual analytics approach, where automated processing and interactive exploration are tightly integrated.

Optimization of patient care at reduced cost requires the orchestration of multiple clinical workflows. Timely getting the imaging/lab tests done and getting the results back to physicians can help quickly diagnose/treat the patient, and save lives. The rapid digitization of diagnostics in radiology and pathology calls for a data-driven optimization of the workflows. Process mining will be used to learn models for the as-is situation. However, process technology will also be used to improve the processes.

Links

Publications

  • Mining process model descriptions of daily life through event abstraction - Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2018). Mining process model descriptions of daily life through event abstraction. In S. Kapoor, R. Bhatia, & Y. Bi (Eds.), Intelligent Systems and Applications: Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2016 (pp. 83-104). (Studies in Computational Intelligence; Read More ...
  • Event abstraction for process mining using supervised learning techniques - Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2018). Event abstraction for process mining using supervised learning techniques. In Y. Bi, S. Kapoor, & R. Bhatia (Eds.), Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2016), 21-22 September 2016, London, United Kingdom (pp. 251-269). (Lecture Notes in Networks and Systems; Read More ...
  • Evaluating conformance measures in process mining using conformance propositions - Syring, A. F., Tax, N., & van der Aalst, W. M. P. (2019). Evaluating conformance measures in process mining using conformance propositions. In M. Koutny, L. Pomello, & L. M. Kristensen (Eds.), Transactions on Petri Nets and Other Models of Concurrency XIV (pp. 192-221). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Read More ...
  • Interactive process mining - Dixit, P. A. M. (2019). Interactive process mining Eindhoven: Technische Universiteit Eindhoven
  • Mining insights from weakly-structured event data - Tax, N. (2019). Mining insights from weakly-structured event data Eindhoven: Technische Universiteit Eindhoven
  • Mining local process models and their correlations - Genga, L., Tax, N., & Zannone, N. (2019). Mining local process models and their correlations. In M. van Keulen, P. Ceravolo, & K. Stoffel (Eds.), Data-Driven Process Discovery and Analysis – 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Revised Selected Papers (pp. 65-88). (Lecture Notes in Business Information Processing; Vol. 340). Cham: Springer. DOI: Read More ...
  • Mining local process models with constraints efficiently: applications to the analysis of smart home data - Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2018). Mining local process models with constraints efficiently: applications to the analysis of smart home data. In Proceedings of the 14th International Conference on Intelligent Environments (IE) (pp. 56-63). [8595032] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/IE.2018.00016 Abstract Sequential pattern Read More ...
  • Generating time-based label refinements to discover more precise process models - Tax, N., Alasgarov, E. E., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2019). Generating time-based label refinements to discover more precise process models. Journal of Ambient Intelligence and Smart Environments, 11(2), 165-182. DOI: 10.3233/AIS-190519 Abstract Process mining is a research field focused on the analysis of event data with the aim Read More ...
  • Alarm-based prescriptive process monitoring - Teinemaa, Irene, Tax, Niek, de Leoni, Massimiliano, Dumas, Marlon & Maggi, Fabrizio Maria (2018). Alarm-based prescriptive process monitoring. In Ingo Weber, Jan vom Brocke, Marco Montali & Mathias Weske (Eds.), Business Process Management Forum – BPM Forum 2018, Proceedings (pp. 91-107). (Lecture Notes in Business Information Processing, No. 329). Springer. Abstract Predictive process monitoring is Read More ...
  • Indulpet miner : combining discovery algorithms - Leemans, Sander J.J., Tax, Niek & ter Hofstede, Arthur H.M. (2018). Indulpet miner : combining discovery algorithms. In Dumitru Roman, Henderik A. Proper, Robert Meersman, Hervé Panetto, Christophe Debruyne & Claudio Agostino Ardagna (Eds.), On the Move to Meaningful Internet Systems. OTM 2018 Conferences – Confederated International Conferences (pp. 97-115). (Lecture Notes in Computer Science Read More ...

Staff

  • Boudewijn van Dongen - 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 Read More ...
  • Natalia Sidorova - Dr. Natalia Sidorova is assistant professor at the PA 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 Read More ...
  • Eric Verbeek - Eric is the scientific programmer in the PA group. As such, he is the custodian of the process mining framework ProM. In you want access to the ProM repository, or have any questions related to ProM and its development, ask Eric. Recently, he has been working on a decomposition framework for both process discovery as Read More ...

Former staff

  • Alok Dixit - Position: PhD Student Room: MF 7.108 Tel (internal): – Links: Google scholar page Scopus page TU/e employee page Projects Publications Presentations
  • Bart Hompes - Bart is an enthusiastic, fast-learning team-player eager to learn and develop new technologies. Bart’s broad interests lie in the fields where business meets technology, such as BPM, BI and BIS. Bart likes to work in international, research-oriented environments with relations to practical applications and implementation. In his spare time Bart likes to ride my motorcycle Read More ...
  • Niek Tax - Niek is a PhD student within the PA group where his main research is in the area of process mining. More concretely, his research interests include seasonality detection, deviation detection, predictions and recommendations based on process mining techniques. Position: PhD Student Room: MF 7.108 Tel (internal): 8965 Links: Personal home page Google scholar page Scopus Read More ...
  • Wil van der Aalst - Prof.dr.ir. Wil van der Aalst is a full professor of the Process and Data Science (PADS) group at the RWTH in Aachen (Germany) and a part-time professor in the PA group. His personal research interests include process mining, business process management, workflow management, Petri nets, process modeling, and process analysis. Position: HGL Room: MF 7.064 Read More ...
  • Joos Buijs - 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 Read More ...
  • Marie Koorneef - Position: PhD Student Room: MF 7.109 Tel (internal): 7393 Links: TU/e employee page Projects

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