# Towards a Digital Transformation Hyper-Framework:
## 3 Materials and Methods
### The Foundation of Additional Hypotheses (H2 to H5)
#### 3.2.2 The Executable Virtualization Ability (H3)  
The efficient and effective use of digital technology infrastructure is essential in current enterprise architectures. Two diverse approaches that aid the transparency of software services in a heterogeneous environment are commonly available: containerization and virtualization. Containers are portable, stand-alone executable units that pack together software asset and all the necessary resources to enable independent running in the arbitrary computer environments. The Docker is currently the most widely utilized containerization tool, while the Kubernetes actually represents an industry standard for containers orchestration. The representative container applications include building the Microservice architectures, continuous integration, continuous deployment, hybrid and multi cloud configuration support, the deployment of artificial intelligence and machine learning applications, and coupling the large language models (LLMs) with the related Generative Artificial Intelligence mechanisms [78]. On the other hand, virtualization assumes replacing the physical computers with software-defined virtual machines (VMs), operated under the control of the VMs manager (hypervisor), enabling the automatic creation of service management workflows by superposing the current configuration-instance portfolio and the current user's profile (service policies) [79]. While VM virtualizes the hosting machine resources, container virtualizes the hosted operating system. Both mechanisms support the deployment of software services over digital technology infrastructure and enable the run-time separation of the logical operation context (virtual twin) and the physical execution context (physical twin). Hypothesis 3 (H3) states that each configuration instance has to be an executable virtual entity capable of real-time emulation (imitate with another system, surrogate), simulation (model-based prediction), operation (traceable running), monitoring (noninvasive and secured), modeling, decision-support mechanism, access (User Interface), and evaluation (User Experience) of configured components structure and behavior. The DgTHyF needs to support an open set of modeling and simulation technologies and tools that, packed with the software asset, enable the transparent execution over the entire digital technology infrastructure. The contemporary representative repertoire of modeling techniques and simulation tools, adapted from [80], is presented in Figure 8.  
3.2.3. Hiding the Data-Layer Complexity (H4)  
Digitally transformed systems exhibit a dramatic growth in volumes and diversity of generated, stored, retrieved, and processed data harvested from different sources and preserved in a distributed data repository (a collection of multiple interconnected data chunks, physically spread across hosting computer network infrastructure, forming a complex data layer). The emerging complexity demands efficient handling and represents one of the most challenging aspects of successful software development. Engineering rationality prefers data layer homogeneity with uniformity (All nodes utilize the same DBMS software and possess identical database schemas), consistency (Changes made to the database on one node are automatically propagated to other nodes, ensuring data consistency), and simplicity (The same DBMS software exists throughout the data layer) as the primary beneficial features. Unfortunately, the evolution of digitalization in cyber-physical-socio-technology systems has led to natural heterogeneity (the coexistence of structured, semi-structured, and unstructured data).