Problem
Research questions and friction points this paper is trying to address.
Probabilistic Forecasting
Business Process Executions
Neural Temporal Point Processes
Predictive Distributions
Service-based Systems
Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes
🤖 AI Summary
该研究使用神经时序点过程解决服务系统中业务流程执行的预测问题，提供概率性预测并改进了时间戳相同事件的处理方法。
📝 Abstract
Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability is known. Mainstream deep-learning models for this task are discriminative and deterministic: they emit a single next activity and a single remaining-time estimate, without a distribution to reason over. We instead cast the problem as generative sequence modelling with marked temporal point processes, which define a joint density over the next mark and its inter-event time and therefore deliver predictive distributions by construction. Real event logs violate the simple-point-process assumption these models rest on, since consecutive events frequently carry identical timestamps; we handle such ties explicitly and combine a transformer encoder with a mixture decoder over inter-event times, trained by exact log-likelihood. On ten public logs, the resulting model matches discriminative baselines on point accuracy, dominates them on the calibration and sharpness of remaining-time distributions, and is the cheapest at inference, since a full predictive distribution is obtained in a single forward pass without sampling.
Innovation
Methods, ideas, or system contributions that make the work stand out.
neural temporal point processes
generative sequence modelling
transformer encoder
mixture decoder
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