Event knowledge graphs (EKG) extend the classical notion of a trace to capture multiple, interacting views of a process execution. In this paper, we tackle the open problem of automating EKG discovery from uncurated data through a principled probabilistic framing based on the outcome space resulting from featured-derived partial orders on events. From this we derive an EKG discovery algorithm based on statistical inference rather than an ad hoc or heuristic-based strategy, or relying on manual analysis from domain experts. This approach comes at the computational cost of exploring a large, non-convex hypothesis space. In particular, solving the maximum likelihood term in our objective function involves counting the number of linear extensions of posets, which in general is #P-complete. Fortunately, bound estimates suffice for model comparison, and admit incorporation into a bespoke branch-and-bound algorithm. We establish an upper bound on our objective function which we show to be antitonic w.r.t. search depth for branching rules that are monotonic w.r.t. model inclusion. This allows pruning of large portions of the search space, which we show experimentally leads to rapid convergence toward optimal solutions that are consistent with manually built EKGs.

翻译：事件知识图谱（EKG）扩展了传统迹的概念，以捕捉过程执行中多个交互视角。本文通过基于事件特征导出偏序所产生结果空间的概率化建模框架，解决了从未经整理数据中自动化发现EKG这一开放性问题。由此我们推导出一种基于统计推断而非临时启发式策略或依赖领域专家人工分析的EKG发现算法。该方法需要承担探索大规模非凸假设空间的计算代价。特别地，求解目标函数中的最大似然项涉及计算偏序集的线性扩展数量，该问题在一般情况下属于#P完全问题。幸运的是，边界估计已足以支持模型比较，并可融入定制的分支定界算法。我们建立了目标函数的上界，并证明该上界对于模型包含单调的分支规则具有关于搜索深度的反单调性。这使得搜索空间的大规模剪枝成为可能，实验表明该方法能快速收敛至与人工构建EKG相一致的最优解。