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的成熟度进行量化的可能性的研究。其主要目标是制定具有明确语义的措施，并可追溯到可观察或可估计的过程和产品数量。所有产生的不确定性，无论是任意的和认识的，因此被视为通过概率论。这种务实的态度也与《测量不确定度表示指南》（GUM）相一致。为了持续评估生产工艺成熟度和该研究单位内的加速成熟度，遵循一个重要原则：整个过程中出现的数据、信息和知识（DIK）的所有方面都将因此被正式化、结构化、组织化并存储在所谓的虚拟工艺档案（VPD）中。过程的演变和所有模型、控制算法等都存储在VPD中，以便系统地重复使用，自动推理和推理可以应用于VPD。VPD将被组织为一个巨大的知识图，在此基础上可以执行有效的搜索、查询、推理和推理，并为过程专家提供决策支持。科学的挑战是由于DIK的异质性和集成的物理先验知识和定性推理方法到语义知识表示和查询处理。

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.