**1. Introduction**

The globally accepted definitions of digitalization and digital transformation do not still exist, although the terms are in the field for quite a long time. In Gartner Glossary, digitalization is defined as *the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business* [1]*.* This definition accents the higher granularity mission concerning global system aspects. Considering the particular enterprise systems, that have decided to move their business into the digital form, there is a challenging activity of developing a completely new set of processes and procedures in compliance with the formulated digital business model. This process is usually considered as enterprise digital transformation. The proliferation of Information and Communication Technologies (ICT), especially in the last decade, puts digitalization and digital transformation into the focus of arbitrary systems development and sustainable collaboration/cooperation in the context of a huge number of dynamic configurations that may emerge throughout its entire life cycle.

The interoperability of heterogeneous systems and integration processes favor System of Systems (SoS) analysis and synthesis approaches to dominate through the 21st century, a century that has become the era of complexity. From the architectural point of view, every real-life system is an SoS, where its architecture is usually seen as a mechanism that creates the illusion of simplicity. To avoid the traditional hierarchical approach to the representation of systems internals, the SoS metaphor has to be deeply understood due to its inherent meshed topology. SoS may range from the technical-systems counterparts, dominantly addressed in systems engineering, to the social systems like, for example, the education, teaching, learning, and performing ecosystem that favor personal competency profile, that suits the industry 4.0 competencies compliance. An integrated systems approach needs to provide a framework and language that allow different systems, with highly divergent characteristics, to interoperate in favor of the commonly agreed mission. With the natural multidimensionality, embedded in the generic SoS paradigm, the underlining complexity directly affects the management and control of the resulting SoS. As a consequence, the dynamic multilayered architecture emerges as a promising approach to the internals complexity hiding.

The SoS concept assumes that participating systems are characterized by the: autonomous mission; independence of encapsulated operations; the difference in fundamental life cycle model aspects. The SoS life cycle stages generally include *creation* (that usually strictly separates the creation process from operational usage); *sustainable operation* (leaving the variety of SoS artifacts persistent instances); *migration* (SoS evolution that retains functionality and structure while replacing the supportive technologies); *replacement* (complete or partial component replacement while preserving the interoperability over specified interfaces); and *termination* (the retirement of a component(s) system(s) while preserving the structural and functional consistency of the remaining SoS). The SoS level of integration is dominantly differentiated by the way the participating systems are orchestrated. According to ref. [2] SoS may be divided into four main categories: *centrally* (directed); *cooperatively* (acknowledged); *collaboratively* (with common enforcing mechanisms); *emergency control* (virtual control of uncontrolled large-scale behavior) *orchestrated*. To cope with the complexity, the architectural design tends to hide the internals of complex systems by wrapping them with an adaptation layer that, to the outer layer stakeholders, exhibits only high-level granularity concepts through well-defined interfaces.

In general, complex systems are analyzed from two complementary aspects: the structure (static view) and the behavior (dynamic view). The structural complexity emerges from observing the system as a composition of arbitrary, interrelated components (subsystems). These components may be considered or sometimes really are systems with a high degree of autonomy. From the behavioral aspect, complexity is defined as the degree of difficulty in predicting the future dynamic properties and behavior of an overall system assuming that current dynamic properties and behavior of the participating components (subsystems) are known. Due to the emergent nature of a large collection of locally interacting components, the properties and the behavior of a collection may not be fully understood or predicted even the full knowledge of its

## *The Foundation for Open Component Analysis: A System of Systems Hyper Framework Model DOI: http://dx.doi.org/10.5772/intechopen.103830*

constituents is available. Because of that, Complex Systems Science (CSS) requires new mathematical and heuristic frameworks and scientific methodologies to cope with SoS organized structural and behavioral complexity handling.

The complementary approach is to view an arbitrary system through its inherent dimensions, which do not strictly structure or behavior-oriented but add another direction of complexity, the multidimensionality. Combined with the multilevel architecture it creates multidimensional multilevel (hyper) composites. If there is a need for different contexts or configurations management in timed framed scale, the hyper composites may generate an arbitrarily large number of instances that are context, configuration, and time-dependent. These instances form a generic repository that is usually seen as an association of Large Data Objects (LDOs). These objects need to be stored, loaded, searched, modified, and visualized in vide variety of ways. Traversing, for example, the hyper-structure instances, with any rational reason in mind, creates a challenging NP (nondeterministic polynomial time) complex problem that opens the fundamental question of arbitrary dimensionality reduction approach. The dimensionality reduction facilitates the transformation of the initial system/ problem into a less complex one (with a lower degree of freedom) that may be efficiently/effectively solved with acceptably lower accuracy (precision).

Modeling is considered a promising approach to cope with complexity. Modeldriven SoS development is a challenging paradigm that may generate the initial multidimensional and multilayered meta-SoS model (MSoSM). The originating version of any particular MSoSM may serve as a starting orchestration pattern that, by dynamical inclusion of individual dimensions in particular configuration(s), forms meta structures and facilitates their instantiation. According to that, one of the main challenging approaches, being the main goal of this chapter, is the specification of SoS based open framework model that may serve as the meta-SoS pattern for describing and monitoring arbitrary complex composites that instantiate multilayered multidimensional data objects that may serve as a resources pool for arbitrary component analysis support.

The openness as a global system property is elaborated in remarkable work on Physics of Open Systems (POS) where the complexity of systems is perceived from systems dynamics (a complexity of movement) [3]. The described POS scientific methods and technologies enable the formation of scientifically proven systems ontological knowledge from its empirical descriptions embedded in a huge amount of semi-structured, multimodal, multidimensional, and heterogeneous data.

The main question arises: *Is it possible to upraise the SoS architecture as a reflection of successive and self-improve dimension reduction processes concerning basic POS principles applied on the persistent information resources base that preserves the SoS dynamics descriptions in the form of LDOs?*

Gaining a definitive answer to this question is far beyond the scope of this chapter. Its intended mission is to formulate a starting SoS-based framework model that may be further dynamically improved to better suit the static structure and dynamic behavior of complex SoS configurations. That is the reason why the reflexive integration of POS and different dimensional reduction methods, through an interoperability framework, have been proposed as the main contribution of this research chapter.

The rest of the chapter is organized as follows: Section 2—The background and motivation elaborate problem domain aspects of SoS multilevel multidimensional and representation, interoperability and integration aspects, architectural challenges, and framework-based approach with selected related work analysis and discussion. Section 3—System of Systems Hyper Framework Model (SoS-HFM)—states and

elaborates the proposed model as a foundation for arbitrary component analysis method application. It is intended to serve as a reliable LDOs pool for incremental dimension reduction activities that converge to the SoS architecture upraise. Section 4— Conclusion is devoted to the concluding remarks, challenges, and the directions of future research activities. In the Appendices section, there are several Java code templates of SoS-HFM concepts presented. References—contains a list of references that have been analyzed and used as the problem domain origins and comparative research results evaluation basis.
