As the promotion of technologies and applications of Big Data, the research of business process management (BPM) has gradually deepened to consider the impacts and challenges of big business data on existing BPM technologies. Recently, parallel business process mining (e.g. discovering business models from business visual data, integrating runtime business data with interactive business process monitoring visualisation systems and summarising and visualising historical business data for further analysis, etc.) and multi‐perspective business data analytics (e.g. pattern detecting, decision‐making and process behaviour predicting, etc.) have been intensively studied considering the steep increase in business data size and type. However, comprehensive and in‐depth testing is needed to ensure their quality. Testing based solely on existing business processes and their system logs is far from sufficient. Large‐scale randomly generated models and corresponding complete logs should be used in testing. To test parallel algorithms for discovering process models, different log completeness and generation algorithms were proposed. However, they suffer from either state space explosion or non‐full‐covering task dependencies problem. Besides, most existing generation algorithms rely on random executing strategy, which leads to low and unstable efficiency. In this paper, we propose a novel log completeness type, that is, #TAR completeness, as well as its generation algorithm. The experimental results based on a series of randomly generated process models show that the #TAR complete logs outperform the state‐of‐the‐art ones with lower capacity, fuller dependencies covering and higher generating efficiency.

中文翻译：

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一种新颖的事件日志完整性定义及相应的生成算法

随着大数据技术和应用的发展，业务流程管理（BPM）的研究已逐渐深入，以考虑大业务数据对现有BPM技术的影响和挑战。最近，并行业务流程挖掘（例如，从业务可视数据中发现业务模型，将运行时业务数据与交互式业务流程监视可视化系统集成，汇总和可视化历史业务数据以进行进一步分析等）和多角度业务数据分析（例如，考虑到业务数据大小和类型的急剧增加，已经对模式检测，决策制定和过程行为预测等进行了深入研究。但是，需要进行全面且深入的测试以确保其质量。仅基于现有业务流程及其系统日志的测试还远远不够。测试中应使用大规模随机生成的模型和相应的完整日志。为了测试用于发现过程模型的并行算法，提出了不同的日志完整性和生成算法。但是，它们会遭受状态空间爆炸或无法完全覆盖的任务依赖性问题的困扰。此外，大多数现有的生成算法都依赖于随机执行策略，这导致效率低下和不稳定。在本文中，我们提出了一种新颖的日志完整性类型，即#TAR完整性及其生成算法。根据一系列随机生成的过程模型的实验结果表明，＃TAR完整日志的性能优于容量较低的最新日志，