TY - JOUR AU - Senderovich, Arik AU - Beck, J. Christopher AU - Gal, Avigdor AU - Weidlich, Matthias PY - 2019/07/17 Y2 - 2024/03/28 TI - Congestion Graphs for Automated Time Predictions JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v33i01.33014854 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4413 SP - 4854-4861 AB - <p>Time prediction is an essential component of decision making in various Artificial Intelligence application areas, including transportation systems, healthcare, and manufacturing. Predictions are required for efficient resource allocation and scheduling, optimized routing, and temporal action planning. In this work, we focus on time prediction in <em>congested</em> systems, where entities share scarce resources. To achieve accurate and explainable time prediction in this setting, features describing system congestion (<em>e.g.</em>, workload and resource availability), must be considered. These features are typically gathered using process knowledge, (<em>i.e.</em>, insights on the interplay of a system’s entities). Such knowledge is expensive to gather and may be completely unavailable. In order to automatically extract such features from data without prior process knowledge, we propose the model of <em>congestion graphs</em>, which are grounded in queueing theory. We show how congestion graphs are mined from raw event data using queueing theory based assumptions on the information contained in these logs. We evaluate our approach on two real-world datasets from healthcare systems where scarce resources prevail: an emergency department and an outpatient cancer clinic. Our experimental results show that using automatic generation of congestion features, we get an up to 23% improvement in terms of relative error in time prediction, compared to common baseline methods. We also detail how congestion graphs can be used to explain delays in the system.</p> ER -