Predicting Case Suffixes With Activity Start and End Times: A Sweep-Line Based Approach

2025-09-17

AI Summary

Existing case suffix prediction methods output only a single timestamp (e.g., completion time), which is insufficient for resource capacity planning—requiring precise characterization of the temporal interval during which resources are occupied. To address this, we propose a novel joint prediction framework that simultaneously forecasts activity sequences and dual timestamps (start and end times), thereby modeling waiting and processing times in tandem. We innovatively introduce a sweep-line mechanism to capture cross-case resource contention effects on waiting time at the process level. Additionally, we design a multi-model architecture that decouples and jointly optimizes sequence generation and time prediction tasks. Experiments on real-world and synthetic datasets demonstrate substantial improvements in time-interval prediction accuracy; the multi-model variants consistently outperform single-model baselines. Our approach thus provides more reliable temporal support for real-time resource scheduling in business processes.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Time-Series/Data Streams
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Predictive process monitoring techniques support the operational decision making by predicting future states of ongoing cases of a business process. A subset of these techniques predict the remaining sequence of activities of an ongoing case (case suffix prediction). Existing approaches for case suffix prediction generate sequences of activities with a single timestamp (e.g. the end timestamp). This output is insufficient for resource capacity planning, where we need to reason about the periods of time when resources will be busy performing work. This paper introduces a technique for predicting case suffixes consisting of activities with start and end timestamps. In other words, the proposed technique predicts both the waiting time and the processing time of each activity. Since the waiting time of an activity in a case depends on how busy resources are in other cases, the technique adopts a sweep-line approach, wherein the suffixes of all ongoing cases in the process are predicted in lockstep, rather than predictions being made for each case in isolation. An evaluation on real-life and synthetic datasets compares the accuracy of different instantiations of this approach, demonstrating the advantages of a multi-model approach to case suffix prediction.

Problem

• Predicting case suffixes with activity start and end timestamps
• Enabling resource capacity planning through time-aware predictions
• Using sweep-line approach for multi-case synchronized prediction

Innovation

• Predicts activity start and end timestamps
• Uses sweep-line approach for multi-case prediction
• Combines waiting and processing time forecasts