标题： 一种可扩展且接近最优的长轨迹一致性检查方法 显示英文标题

标题： A Scalable and Near-Optimal Conformance Checking Approach for Long Traces

摘要： 长轨迹和来自传感器及预测模型的大型事件日志在我们数据丰富的世界中变得越来越常见。 在这种情况下，符合性检查是流程挖掘中的关键任务，由于找到最佳对齐的指数复杂性，可能会变得计算上不可行。 本文介绍了一种新的滑动窗口方法，以解决这些可扩展性挑战，同时保持基于对齐方法的可解释性。 通过将轨迹分解为可管理的子轨迹，并迭代地将每个子轨迹与流程模型对齐，我们的方法显著减少了搜索空间。 该方法使用捕获轨迹和流程模型结构特性的全局信息来做出有根据的对齐决策，即使某些对齐对于局部子轨迹是最优的，也会丢弃那些没有前景的对齐。 这提高了结果的整体准确性。 实验评估表明，所提出的方法在大多数情况下能够持续找到最优对齐，并突出了其可扩展性。 这一结论进一步得到了理论复杂性分析的支持，该分析显示了与其它常见的符合性检查方法相比，搜索空间的增长有所减少。 这项工作为大规模流程挖掘应用中的高效符合性检查提供了有价值的贡献。 显示英文摘要

摘要： Long traces and large event logs that originate from sensors and prediction models are becoming more common in our data-rich world. In such circumstances, conformance checking, a key task in process mining, can become computationally infeasible due to the exponential complexity of finding an optimal alignment. This paper introduces a novel sliding window approach to address these scalability challenges while preserving the interpretability of alignment-based methods. By breaking down traces into manageable subtraces and iteratively aligning each with the process model, our method significantly reduces the search space. The approach uses global information that captures structural properties of the trace and the process model to make informed alignment decisions, discarding unpromising alignments even if they are optimal for a local subtrace. This improves the overall accuracy of the results. Experimental evaluations demonstrate that the proposed method consistently finds optimal alignments in most cases and highlight its scalability. This is further supported by a theoretical complexity analysis, which shows the reduced growth of the search space compared to other common conformance checking methods. This work provides a valuable contribution towards efficient conformance checking for large-scale process mining applications.