Process Conformance under Incomplete Information

流程感知的信息系统协调一组基本操作的执行，以实现业务目标，其中的操作可能像函数调用一样细粒度，也可能像复杂业务事务一样粗粒度。这类系统的行为通常用过程模型来描述。然而，一旦数据在运行期间被记录下来，通常以日志或事件流的形式记录下来，一致性问题就出现了：系统的建模行为和记录的行为是如何相互关联的？回答这个问题是检测、解释和补偿面向过程系统的模型与其实际执行之间的任何偏差的基础。在过程自动化、数据感知和与过程相关的资源的大规模仪器仪表等趋势的驱动下，事件数据的数量及其生成的频率在当今世界正在增加。因此，基于过程执行的完整历史记录的事后一致性检查不再是可行的选择。ProCI项目旨在为一致性检查提供基础，它打破了对事件数据进行全面访问的普遍假设，并支持在不完整信息下进行推理。具体来说，将开发用于抽样和在线一致性检查的模型和算法。鉴于常见一致性检查技术的时间复杂度呈指数级增长，前者只考虑大量事件数据中的一小部分，从而在运行时实现了巨大的改进。后者针对的是在事件流而不是静态日志上实现一致性检查时的空间效率问题。在任何一种情况下，一致性检查都是有损的，它基于对流程事件数据的不完整视图，并且需要分别使用日志的每个新样本或事件流的批处理更新偏差描述。因此，研究的核心挑战将是：(1)为部分一致性结果的推理设计正式的基础，(2)为抽样和在线一致性检查设计算法，为数据不完整引起的偏差提供统计保证，以及(3)实现对底层数据分布变化的鲁棒性。将进行实验验证，以经验证明与假设完全访问事件数据的最先进算法相比，在运行时和空间效率方面取得的改进。

Process-aware information systems coordinate the execution of a set of elementary actions to reach a business goal, where actions may be as fine-grained as function calls or as coarse-grained as complex business transactions. The behaviour of such systems is commonly described by process models. However, once data is recorded during runtime, typically in the form of logs or streams of events, the question of conformance emerges: how do the modelled behaviour of a system and its recorded behaviour relate to each other? Answering this question is the basis for the detection, interpretation, and compensation of any deviation between a model of a process-oriented system and its actual execution. Driven by trends such as process automation, data sensing, and large-scale instrumentation of process-related resources, the volume of event data and the frequency at which it is generated is increasing in today's world. Post-mortem conformance checking based on a complete history of a process' execution is thus no longer a viable option. The ProCI project sets out to provide the foundations for conformance checking that breaks with the omnipresent assumption of comprehensive access to event data and enables reasoning under incomplete information. Specifically, models and algorithms will be developed for sampled and online conformance checking. Given the exponential time complexity of common conformance checking techniques, the former strives for drastic improvements in runtime by considering only a fraction of large volumes of event data. The latter targets the question of space efficiency when conformance checking is realised over streams of events rather than static logs. In either case, conformance checking will be lossy, grounded on an incomplete view on the event data of a process, and needs to update a description of deviations with each new sample of a log or batch of an event stream, respectively. The central research challenges therefore will be (1) to devise the formal foundations for reasoning about partial conformance results, (2) to devise algorithms for sampled and online conformance checking, giving statistical guarantees on the bias induced by data incompleteness, and (3) to achieve robustness against changes in underlying data distributions. Experimental validations will be performed to empirically demonstrate the achieved improvements in runtime and space efficiency compared to state-of-the-art algorithms that postulate complete access to event data.