F: Management and quantification of maturity improvement

成熟度改进的管理和量化涉及基于系统理论和概率方法对过程实例成熟度进行量化的可能性的研究。其主要目标是开发具有清晰语义并且可以追溯到可观察或可评估的过程和产品数量的度量。所有产生的不确定性，无论是任意性的，还是认识性的，都是用概率论的方法处理的。这种务实的态度也符合《测量不确定度表示指南》(GUM)。为了持续评估生产过程成熟度和该研究单位的加速成熟度，遵循一项重要原则：因此，整个过程中出现的数据、信息和知识(DIK)的所有方面都将被正式、结构化、组织和存储在所谓的虚拟过程档案(VPD)中。过程的演变和所有模型、控制算法等以系统可重用的方式存储在VPD中，并且可以对其应用自动推理和推理。VPD将被组织为一个巨大的知识图谱，可以在其上执行有效的搜索、查询、推理和推理，并可基于其为过程专家提供决策支持。科学上的挑战是由于DIK的异构性，以及将物理先验知识和定性推理方法集成到语义知识表示和查询处理中。

Management and quantification of maturity improvement involves research into possibilities for quantifying the maturity of process instances based on systems theory and probabilistic methods. The main goal is to develop measures with a clear semantics and which can be traced back to observable or estimable process and product quantities. All arising uncertainties, both aleatoric and epistemic, are treated consequently by means of probability theory. This pragmatic attitude is also in concordance with the Guide to the Expression of Uncertainty in Measurement (GUM). For the continuous estimation of the production process maturity and the accelerated maturation within this research unit, an important principle is pursued: all aspects of the data, information and knowledge (DIK) that arise throughout the procedure will be consequently formalised, structured, organised and stored within a so-called virtual process dossier (VPD). The evolution of the process and all models, control algorithms etc. are stored in the VPD in such a way that it can be systematically reused and automated inference and reasoning can be applied to it. The VPD will be organised as a huge knowledge graph on which effective search, querying, inferences, and reasoning can be performed, and on which decision support for process experts can be based. The scientific challenges are due to the heterogeneity of DIK and the integration of physical prior knowledge and qualitative reasoning approaches into semantic knowledge representation and query processing.