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Why Are We Waiting Discovering Interpretable Models for Predicting Sojourn and Waiting Times. (504.51 kB)

Why Are We Waiting? Discovering Interpretable Models for Predicting Sojourn and Waiting Times.

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conference contribution
posted on 2024-02-19, 09:36 authored by Boris WiegandBoris Wiegand, Dietrich Klakow, Jilles VreekenJilles Vreeken
Queueing models explain waiting times, predict sojourn times and help to identify and avoid bottlenecks. Domain experts usually create these models by intensive handcrafting, often resulting in idealized models not fitting the actual process behavior well. Discovering queueing models from data can alleviate this effort, but existing methods do not suffice as they are unable to model complex queueing behaviors. We propose a novel approach to discover queueing models for interpretable waiting time prediction using a rich modeling language to fit complex processes. We formalize the problem in terms of the Minimum Description Length (MDL) principle, by which the best model gives the best lossless compression. The resulting optimization problem is computationally hard, and hence we propose the greedy CueMin algorithm to efficiently find good queueing models from data. Through an extensive set of experiments including a case study on call center data, we show it discovers inherently interpretable models, which explain and predict behavior of waiting lines better than the state of the art.

History

Editor

Shekhar S ; Zhou Z-H ; Chiang Y-Y ; Stiglic G

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

SIAM International Conference on Data Mining (SDM)

Journal

SDM

Page Range

352-360

Publisher

SIAM

BibTeX

@conference{Wiegand:Klakow:Vreeken:2023, title = "Why Are We Waiting? Discovering Interpretable Models for Predicting Sojourn and Waiting Times.", author = "Wiegand, Boris" AND "Klakow, Dietrich" AND "Vreeken, Jilles", editor = "Shekhar, Shashi" AND "Zhou, Zhi-Hua" AND "Chiang, Yao-Yi" AND "Stiglic, Gregor", year = 2023, month = 4, journal = "SDM", pages = "352--360", publisher = "SIAM" }

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