## 2 Theoretical Fundamentals and State of the Art
As stated by [65; 66; 67], the IIoT furthermore serves as a major enabler for Industry 5.0 focusing on a higher degree of Human-Machine Interaction enabling a virtualized, costumer-driven manufacturing environment [68]. Considering the limited human and architectural IT resources within most SMEs, cloud computing solutions can add significant economic benefits and therefore additionally serve as an accelerator for the digital transformation of these entities [69]. The basic definition of cloud computing is given by the National Institute of Standard and Technology (NIST): _\"a model for enabling convenient, on-demand network access to a shared pool configurable computing resources (e.g., networks, servers, storage, application, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction\"_[70]. A Service Level Agreement (SLA) regulates the services to be provided between the consumer and the provider and the services to be provided [71]. Through the combination of IIot and cloud computing it is possible to implement decentralized, on-demand data computation [59; 72; 73]. As stated by [59], with centralized cloud computation, the probability of potential delays in high-priority data in the event of high data traffic cannot be neglected. However, there is a possibility that highly specialized SMEs, which already have such an infrastructure and know-how, are not willing to outsource computing activities to cloud services due to legal uncertainties regarding data protection and privacy due to different jurisdictions. Despite this uncertainties, decentralized computing resources, in combination with big data, make it possible to monitor and optimize entire process chains in real-time [63]. As already mentioned, through the interaction of networked smart devices subordinate to a CPPS and linked by an IIoT, collected data can be processed through suitable computing resources, such as cloud computing services. To enable regulated access, availability and storage to data, suitable databases and corresponding DBMS must be implemented [3; 26]. When selecting a suitable DBMS, it is important to pay particular attention to the system limits, as data to be processed can be too large for specific DBMSs and thus impair performance [74]. Corresponding database models include relational, object-orientated and document-based databases [75; 76]. Two programming languages capable of structuring and accessing the data of a database are Structured Query Language(SQL) and not-only Structured Query Language (noSQL) [76]. Relational databases store the data in structured tables linked together by keys and can be accessed by SQL [75]. Due to the high amount of links between the tables, performance problems occur with large amounts of data, as the relationship models become increasingly complicated [75]. The less commonly used object-oriented databases manage data in an object which internally takes over the data management [76; 77]. Depending on the database model, data can be accessed with suitable object-orientated languages. Document-oriented databases follow a non-relational approach where data is stored in documents of different formats by an identifier [76; 78]. Data in a document can be accessed with key-value pairs using noSQL [76; 77; 78]. As stated by [78], DBMS based on noSQL are suitable for a big amount of data if the data does not demand a relational model, thus gaining popularity, especially when dealing with unstructured data sets [79]. As stated by [79; 80], which DBMS to use is highly dependent on the use case, software, and requirements. I 4.0 is also changing the security requirements for included systems [81; 82]. With the increased utilization of HMIs in the manufacturing environment, it is essential to verify the validity of the input and trustworthiness of the operator to ensure operational safety [81].