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 …
Spenrath, Y., Hassani, M., van Dongen, B. F., & Tariq, H. (Accepted/In press). Why did my Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. In Proceedings of ECML PKDD 2020 Abstract Transaction analysis is an important part in studies aiming to understand consumer behaviour. The first step is defining a proper measure Read More …
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 …
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 …
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