Meta Model

A Meta Model or surrogate model is a model of a model, and metamodeling is the process of generating such metamodels. Thus, metamodeling or meta-modeling is the analysis, construction, and development of the frames, rules, constraints, models, and theories applicable and useful for modeling a predefined class of problems. As its name implies, this concept applies the notions of meta- and modeling in software engineering and systems engineering. Metamodels are of many types and have diverse applications.

The term "Meta Model" can be interpreted in several contexts across different fields, ranging from linguistics and psychology to information technology and software development. Here's an overview reflecting its multidisciplinary nature:

In Neuro-Linguistic Programming (NLP), a meta-model is a communication tool that helps clarify language by uncovering the underlying meaning in conversations. Developed by Richard Bandler and John Grinder, the meta-model aims to challenge and expand the limits of a person's model of the world. It does this by addressing and questioning their speech's deletions, distortions, and generalizations. The goal is to help individuals understand their thoughts and feelings by making their language more precise and bringing their assumptions to awareness. In Software and Systems Engineering

In software and systems engineering, a meta-model defines the language used to express a model or to model a particular domain. It specifies the structure, semantics, and constraints for a family of models within a specific domain of knowledge or technology. For instance, the Unified Modeling Language (UML) relies on a Meta Object Facility (MOF), which serves as a meta-meta model at the top of the UML meta-modeling hierarchy. This allows for the definition of the UML itself in UML, enabling extensibility and adaptation to specific domains. In Information and Data Management

In information and data management, metamodels describe the structure of data or metadata within systems, providing a schema or architecture that outlines how information is organized and interrelated. This can be particularly useful in data warehousing and enterprise architecture, where understanding the relationships between various data elements and how they can be utilized across the organization is crucial. Meta models help ensure data consistency, understandability, and reusability across different applications and systems. Common Characteristics Across Contexts

• Abstraction: Meta models provide a higher level of abstraction, offering a framework or schema that describes other models or languages.
• Standardization: They often serve as standards for creating models, ensuring consistency and interoperability within and across domains.
• Flexibility: Meta models are designed to be adaptable, allowing customization and extension to meet specific requirements or to cover new application domains.
• Clarity and Precision: Whether in communication or in data structure, meta-models aim to clarify the semantics and improve the precision of the models or languages they describe.

Conclusion

The concept of a Meta Model transcends various disciplines, serving as a foundational framework that guides the creation, interpretation, and analysis of models in fields ranging from psychology and linguistics to software development and information management. By providing a structured modeling approach, meta-models facilitate clearer communication, deeper understanding, and more effective system and data management across diverse domains.

See Also

The term "Meta Model" in various disciplines refers to a model that describes how other models relate or are constructed, providing a framework for understanding and categorizing complex information. In different contexts, such as linguistics, software engineering, and psychology, the meta-model serves distinct purposes but aims to offer a higher level of abstraction for analyzing and generating models within the specific field. Here's how the Meta Model concept applies across several key areas:

In Neuro-Linguistic Programming (NLP)

• NLP Meta Model: A linguistic tool within NLP that clarifies and specifies vague language, uncovers underlying beliefs, and improves communication effectiveness. It involves questioning language patterns to reveal the deeper meaning behind words and phrases.

In Software Engineering

• Meta-Object Facility (MOF): An example of a meta-modeling framework that defines the language and system for specifying, constructing, and managing technology-neutral metamodels. MOF is essential in the development of models and standards for software architecture.

In Data Modeling

• Data Meta Model: Describes the structure of different data models and how they relate, allowing for the translation between different data standards or formats. It's fundamental in database design and the interoperability of information systems.

In Enterprise Architecture

• Enterprise Architecture Frameworks: Such as TOGAF or Zachman Framework, which include meta-models describing the relationships and structures of enterprise architecture components. These frameworks guide the design, planning, implementation, and governance of an enterprise architecture.
• Model Driven Architecture (MDA): A software design approach that uses meta-models to separate the specification of system functionality from the implementation on any specific technology platform.
• Unified Modeling Language (UML) is a standardized modeling language in software engineering that includes a set of graphical notation techniques for creating visual models of software systems.
• Semantic Web Technologies: Such as Resource Description Framework (RDF) and Web Ontology Language (OWL), which use meta-models to describe web resources and their relationships in a way that computers can understand.
• Systems Theory: Discussing the interdisciplinary study of systems, which often uses meta-models to understand and predict the behavior of complex systems.
• Cognitive Psychology and Linguistics: These fields cover the study of mental processes, including how people think, perceive, remember, and learn, as related to the use of meta-models in NLP.
• Information Theory: Discussing the quantification, storage, and communication of information, which may involve meta-models for understanding information systems and their efficiencies.