Yorick Spenrath’s PhD Research Featured in Cursor and Eindhovens Dagblad

Yorick Spenrath has recently been featured in both Cursor and Eindhovens Dagblad for his innovative research on predicting supermarket consumer behavior. Spenrath’s study, which analyzed close to 70 million receipts, focuses on how supermarkets can use mathematical models to understand and forecast customer participation in loyalty programs and promotional campaigns. Source: TU/e sharepoint

Online Prediction Threshold Optimization Under Semi-deferred Labelling

Spenrath, Y., Hassani, M., & van Dongen, B. F. (2024). Online Prediction Threshold Optimization Under Semi-deferred Labelling. In T. Palpanas, & H. V. Jagadish (Eds.), 8th International workshop on Data Analytics solutions for Real-LIfe APplications (DARLI-AP) (CEUR Workshop Proceedings; Vol. 3651). CEUR-WS.org. https://ceur-ws.org/Vol-3651/ Abstract In supermarket loyalty campaigns, shoppers collect stamps to redeem limited-time luxury Read More …

Online Prediction of Aggregated Retailer Consumer Behaviour

Spenrath, Y., Hassani, M., & van Dongen, B. F. (2022). Online Prediction of Aggregated Retailer Consumer Behaviour. In J. Munoz-Gama, & X. Lu (Eds.), Process Mining Workshops – ICPM 2021 International Workshops, Revised Selected Papers (pp. 211-223). (Lecture Notes in Business Information Processing; Vol. 433 LNBIP). Springer. https://doi.org/10.1007/978-3-030-98581-3_16 Abstract Predicting the behaviour of consumers provides valuable Read More …

BitBooster: Effective Approximation of Distance Metrics via Binary Operations

Spenrath, Y., Hassani, M., & Van Dongen, B. F. (2022). BitBooster: Effective Approximation of Distance Metrics via Binary Operations. In H. Va Leong, S. S. Sarvestani, Y. Teranishi, A. Cuzzocrea, H. Kashiwazaki, D. Towey, J-J. Yang, & H. Shahriar (Eds.), Proceedings – 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022 (pp. 201-210). Read More …

Predicting Business Process Bottlenecks In Online Events Streams Under Concept Drifts

Spenrath, Y., & Hassani, M. (2020). Predicting Business Process Bottlenecks In Online Events Streams Under Concept Drifts. In M. Steglich, C. Muller, G. Neumann, & M. Walther (Eds.), Proceedings of European Council for Modelling and Simulation (ECMS) 2020 (pp. 190-196). (Proceedings European Council for Modelling and Simulation; Vol. 34, No. 1). European Council for Modeling Read More …

Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data

Spenrath, Y., Hassani, M., van Dongen, B. F., & Tariq, H. Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. In Y. Dong, D. Mladenic, & C. Saunders (Eds.), Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track – European Conference, ECML PKDD 2020, Proceedings (pp. Read More …

Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts

Spenrath, Y., & Hassani, M. (2019). Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts. In P. Papotti (Ed.), Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference: Lisbon, Portugal, March 26, 2019 (CEUR Workshop Proceedings; Vol. 2322). CEUR-WS.org. Abstract Bottleneck prediction is an important sub-task of process mining that aims at optimizing Read More …

JM0210 Real-Time Process Mining (JADS)

The Real-Time Process Mining course is an advanced master-level process mining course where the following main contents will be covered: Dimensionality reduction and efficient preprocessing of log files Stream data mining Advanced topics in process mining, like: stream process discovery, online conformance checking and concept drift detection When the focus shifts to advanced topics in Read More …