A key concern of automatic process discovery is to provide insights into
performance aspects of business processes. Waiting times are of particular
importance in this context. For that reason, it is surprising that current
techniques for automatic process discovery generate directly-follows graphs and
comparable process models, but often miss the opportunity to explicitly
represent the time axis. In this paper, we present an approach for
automatically constructing process models that explicitly align with a time
axis. We exemplify our approach for directly-follows graphs. Our evaluation
using two BPIC datasets and a proprietary dataset highlight the benefits of
this representation in comparison to standard layout techniques.

自动流程发现关注业务流程性能，其中等待时间至关重要。本文提出了一种能够明确与时间轴对齐的自动构建流程模型的方法，并以直接后继图为例证。通过对两个 BPIC 数据集和一个专有数据集的评估，突出了该表示方法相较于标准布局技术的优势。

基于时间轴的流程发现

Timeline-based Process Discovery

We study dynamic matching in an infinite-horizon stochastic market. While all
agents are potentially compatible with each other, some are hard-to-match and
others are easy-to-match. Agents prefer to be matched as soon as possible and
matches are formed either bilaterally or indirectly through chains. We adopt an
asymptotic approach and compute tight bounds on the limit of waiting time of
agents under myopic policies that differ in matching technology and
prioritization.
We find that the market composition is a key factor in the desired matching
technology and prioritization level. When hard-to-match agents arrive less
frequently than easy-to-match ones (i) bilateral matching is almost as
efficient as chains (waiting times scale similarly under both, though chains
always outperform bilateral matching by a constant factor), and (ii) assigning
priorities to hard-to-match agents improves their waiting times. When
hard-to-match agents arrive more frequently, chains are much more efficient
than bilateral matching and prioritization has no impact.
We further conduct comparative statics on arrival rates. Somewhat
surprisingly, we find that in a heterogeneous market and under bilateral
matching, increasing arrival rate has a non-monotone effect on waiting times,
due to the fact that, under some market compositions, there is an adverse
effect of competition. Our comparative statics shed light on the impact of
merging markets and attracting altruistic agents (that initiate chains) or
easy-to-match agents.
This work uncovers fundamental differences between heterogeneous and
homogeneous dynamic markets, and potentially helps policy makers to generate
insights on the operations of matching markets such as kidney exchange
programs.

研究无限期随机市场中的动态匹配问题，探讨了匹配技术和优先级对等待时间的影响，并发现不同市场结构下的不同优先级与匹配方式，对匹配效率和等待时间有显著差异。