English(EN) Framework for Grouping Local Process Models

新框架对过程挖掘模型进行分组以减少重复

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-07 04:00

提出了一种新框架来应对过程挖掘中的模型爆炸和重复挑战。该框架旨在对局部过程模型（LPM）进行分组，并从每个组中选择代表性模型形成最优样本。该方法使用既定指标或通过比较模型在事件日志中的上下文外观来衡量模型之间的相似性，这些日志是从日志中的数据属性派生出来的。通过在多个事件日志中与仅由得分最高的 LPM 组成的样本进行重复和覆盖率的比较，证明了这种分组策略的有效性。 AI

影响通过减少模型冗余，提高了过程挖掘分析的效率和可解释性。

排序理由关于过程挖掘新框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 关于过程挖掘新框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 86 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Viki Peeva, Wil M. P. van der Aalst · 2026-07-07 04:00

用于分组本地进程模型的框架

arXiv:2607.04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent years, process mining has proved successful in the analy…

本文如何被排名

报道来源 [1]

用于分组本地进程模型的框架

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