DOI: 10.5281/zenodo.8037211
title: Simulated Event Log: Udon-ya

description: This dataset is a simulated event log in standard XES format.<br> It contains events pertaining to restaurant operations of a fictional Udon, a Japanese noodle dish, Restaurant. In Japanese:うどん屋 [udon-ya].<br> There are 16,412 generated cases distributed over the span of one year, containing in total 674,486 events of 36 unique activities.<br> The log makes use of the lifecycle extension, providing <em>start</em> and <em>complete</em> events per event instance (+ <em>schedule</em> for one activity). Timestamps are (semi-)realistically distributed over daily and weekly business patterns. The underlying simulation respects resource availability and capacity and incorporates queueing as its main dynamics-drivers in addition to a specified medium-complexity control flow. There is looping behavior, interleaving concurrency and event attribute-dependent control-flow routing. The event log is comprised of three different restaurant processes: <em>preparation (prep)</em>, store <em>opening/closing </em>and <em>service</em>. <em>prep</em>: In the mornings, senior employees prepare ingredients with long cooking/resting times. <em>opening/closing: </em>Restaurant setup, opening, cleanup and closing performed with higher propensity by part-time employees. <em>service</em>: Customer groups arrive, queue for tables, are seated and order one dish per customer. Then, ordered dishes are prepared concurrently as resource availability permits and served all at once. Finally, the customers at one table eat concurrently but pay and leave the restaurant together (synchronized).<br> There is lunch service ~11:30-14:30 and dinner service ~18:00-21:00 with peak customer rush hours at ~13:00-13:15 and ~18:30-19:00 on weekdays. The restaurant is closed on Mondays. The dish preparation subprocess has a <em>1 to n</em> multiplicity with cases, making the log pseudo-object centric. Dishes have a unique global id which is referenced in all associated events. Organizationally, there are six different employees with varying working hours, capacity and activity assignments, propensities and skill. Activity assignments are based on a division of back of the house (<em>BOH</em>) and front of the house (<em>FOH</em>). There are also additional resources to represent, e.g., the restaurant table capacity. <em>Tenchou-san</em> (Jap. 店長), the shop manager. Up to two times faster than the other cooks. Mostly <em>BOH</em>. <em>Oku-san</em>, the shop manager's wife. Bridge between <em>BOH </em>and <em>FOH</em>. <em>Deshi-san A</em> & <em>B</em>, two skilled cooks. Only <em>BOH</em>. <em>Baito-san A</em> & <em>B</em>, two part-time workers mostly serving customers (<em>FOH</em>). Furthermore, there are five engineered instances of temporary concept drift with varying impact within the overall process span from 2023-04-01 to 2024-03-31. From 2023-06-20 to 2023-07-26: Decreased customer arrival rate and group size, resulting in visibly reduced activity. From 2023-09-01 to 2023-10-22: Slightly increased customer arrival rate and group size. From 2023-10-15 to 2023-11-05: Most important (human) resource becomes unavailable, resulting in increased utilization of compensating resources. From 2023-11-19 to 2023-12-10: Reduced restaurant table capacity, not causing any prominent effects. From 2023-12-10 to 2024-01-15: Increased restaurant table capacity, not causing any prominent effects. The simulation software used is a past version of https://git.rwth-aachen.de/leah.tgu/qprsim, by the same author as this dataset. The repository specifically contains the configuration for this log generation as a Jupyter notebook in <em>sample/udonya.ipynb</em>.