Predicting Business Process Bottlenecks In Online Events Streams Under Concept Drifts

Abstract

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A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques

“…This will be demonstrated in the LSP case study in the next subsection. Manufacturing Visual analytics approach that facilitates interpretability and explainability of process models and bottlenecks [13] Automotive industry Inductive miner and Fuzzy miner with statistical evaluation measures [31] Bank and real state Fuzzy model algorithm [40] Predict Services company Gradual and Recurrent Adaptive Hoeffding Option Forest (GRAHOF) approach [35] Logistics Inductive miner [7] Recommend Building construction Inductive miner and Fuzzy miner [19] Step 3: Realization. After the selection and evaluation of process mining techniques, operational support can be realized by instantiation and systems integration.…”

Deviance Mining of Online Processes with Non-Atomic Events in the COVID-19 Domain

“…Thus, several techniques exist to deal with concept drifts in online scenarios [6]. In particular, some process improvement approaches address concept drifts, such as Spenrath and Hassani [32], who proposed a model to predict bottlenecks in the execution of a process in online data streams that is adapted to the occurrence of concept drifts, so that predictions remain correct. Hassani [14] presented an approach for defining the windows of analysis and their sizes according to the characteristics of the concept drift, so that the data to be analyzed actually represent the process.…”

Dynamically adaptive and diverse dual ensemble learning approach for handling concept drift in data streams

“…Many algorithms have been proposed in literature to deal with a specific type of drift. [4][5][6] Application based concept drift approaches like gradual and recurrent concept drift adapting ensemble classifier (GRAEC) 7 and the work proposed by Spenrath and Hassani, 8 majorly focus on gradual and recurrent drifts in the field of process monitoring and mining. In another work by Katakis et al, 9 drifting scenario of recurring contexts in email filtering has been discussed.…”