Model-Driven Anything 

Author: Tim Ackroyd, TOT Consulting

As I was getting into more advanced styles of modeling from my Architectural Engineering days (70's-80's) with Computer Aided Design (CAD) software, I moved into the 90’s with focus on Computer Aided Software Engineering (CASE) and then later into Unified Modeling Language. The journey started with AutoCAD and DesignCAD, went through Visio, into ArgoUML when UML came about. As I have struggled with my left and right hemisphere thinking, I tend to think in 0’s and 1’s and vectors. Thus, I do better with computer-aided approaches to figuring things out.

Because most of what I did was somehow “model-driven”, I summed up all the approaches into Model-Driven Anything. I believe that everything can be determined using mathematics, yet I am not a mathematician. I have designed everything from architectural visions (building and construction) to advanced accounting systems, to software implementations. When I jumped into Cloud, I designed Data Centers, Network Topologies, Routing/Gateway systems, to software implementations. We had to go from IaaS to SaaS very quickly and collaborate along the way. A few years later, I was thrown into something called Big Data and landed into Data Warehousing, Analytics, and Machine Learning.

To work with all kinds of human resources and so-called experts, and as a Solution Architect/Engineer (problem solver), I had to use pictures and content to bring technical and non-technical people together. UML became my primary language!

Model-Driven Architecture

In 2000, a co-worker asked me if I heard about MDA. My answer was yes, and he said we need to do this in our company. I gladly led that expedition.

Model Driven Architecture (MDA) is an approach to software development that emphasizes the use of models to drive the design, implementation, and maintenance of software systems. It is a framework introduced by the Object Management Group (OMG) that aims to improve productivity, maintainability, and interoperability in software development.

The key principles of Model Driven Architecture include:

1. Platform-Independent Model (PIM): MDA starts with the creation of a platform-independent model that represents the system's functionality, structure, and behavior without being tied to any specific technology or implementation platform. The PIM focuses on capturing the essential aspects of the system using high-level abstractions.

2. Platform-Specific Models (PSM): From the PIM, platform-specific models are derived that take into account the specific implementation technologies and platforms. These models include detailed information about the target platform, such as the programming language, frameworks, and libraries to be used. The PSMs provide the necessary specifications for generating the actual software artifacts.

3. Model Transformation: MDA relies on automated model transformations to bridge the gap between platform-independent and platform-specific models. Model transformations are rules or algorithms that convert models from one level of abstraction to another, ensuring that the implementation details are properly captured while preserving the integrity of the system's design.

4. Code Generation: With the platform-specific models in place, code generation tools are employed to automatically produce the executable code, database schemas, configuration files, and other artifacts required for the implementation. Code generation simplifies and speeds up the development process, as a significant portion of the system's code is derived from the models.

5. Model Repository: MDA emphasizes the centralization and management of models within a model repository. The model repository acts as a centralized store where models can be versioned, shared, and accessed by different stakeholders. It allows for collaborative modeling and facilitates the reuse of models and model transformations across projects.

By following the MDA approach, software developers can focus on creating high-level models that capture the system's essence and design intent. The models can then be transformed and translated into platform-specific implementations, reducing the effort required for low-level coding and ensuring consistency between the design and the implementation.

MDA promotes the separation of concerns and the modularization of system functionality, enabling better maintainability, scalability, and interoperability. It also allows for flexibility in adapting to changes in technology or platforms by modifying the platform-specific models and regenerating the implementation artifacts.

Note that MDA is a framework and set of principles, and there are different modeling languages and tools available that support the MDA approach, such as the OMG's own modeling language called the Unified Modeling Language (UML).

Model-Driven Development

Because I always wanted to be a Software Engineer/Developer, but was always promoted into executive roles managing people instead of code, I was not able to advance in the Software Developer field. I got most of my code experience from SourceForge (90’s) and later from GitHub. With modeling tools such as ArgoUML and Sparx EA, I could reverse-engineer code to better understand and use. Today, I understand 14 software languages and several Data Technologies. I am not saying I am the best at any of them, but it’s like my Spanish… I know enough to be dangerous.

Model Driven Development (MDD) is an approach to software development that emphasizes the use of models throughout the entire development lifecycle. It is closely related to Model Driven Architecture (MDA) but places a stronger emphasis on the model-driven aspect of the development process.

In Model Driven Development, models play a central role in capturing the system's requirements, design, and behavior. The development process revolves around creating, refining, and transforming these models to generate software artifacts.

Key aspects of Model Driven Development include:

1. Model-Centric Development: The development process is centered around models. Models are used to capture the system's requirements, architecture, and design. These models serve as the primary artifacts for communication and collaboration between stakeholders, including domain experts, analysts, designers, and developers.

2. Automatic Code Generation: Models are used as input to automated code generation tools. These tools transform the models into executable code or other implementation artifacts, such as database schemas or configuration files. Code generation simplifies the implementation process and ensures consistency between the design and the implementation.

3. Model Transformations: Model transformations are used to convert models from one level of abstraction to another or to transform models between different modeling languages. Model transformations help bridge the gap between the various stages of the development process, enabling the refinement and specialization of models for different purposes and platforms.

4. Round-Trip Engineering: Model Driven Development supports round-trip engineering, which means that changes made to the models can be reflected back into the generated code, and changes made in the code can be synchronized with the models. This bidirectional synchronization allows developers to switch between working directly with code and working with models as needed.

5. Model Verification and Validation: Models can be subjected to various analysis techniques and automated tools to verify and validate their correctness, consistency, and compliance with requirements. Model validation helps identify potential issues early in the development process and ensures the quality of the generated code.

By employing Model Driven Development, organizations can achieve several benefits, including increased productivity, reduced development time, improved maintainability, and better alignment between the system's design and the desired functionality.

It's important to note that Model Driven Development is a concept and set of principles, and there are different modeling languages, tools, and frameworks available to support the approach. These tools and languages provide the infrastructure to create, manipulate, and transform models effectively throughout the development process.

Model-Driven Anything

So today, while you cannot find much on Google, there is a lot of varying views on Model-Driven Anything, and below are some of them.

Model Driven Anything (MDA) is a broad concept that extends beyond software development and encompasses various domains where models are used as central artifacts to drive different processes. The term "Model Driven Anything" is often used to describe the application of model-driven approaches in different contexts, such as software development, system engineering, business process management, and more.

The core idea behind Model Driven Anything is the use of models as the primary means of capturing, representing, and manipulating information. Models provide a higher-level abstraction that simplifies complex systems, processes, or domains, making them easier to understand, communicate, and analyze.

In different domains, Model Driven Anything can refer to:

1. Model Driven Software Development: This is the most common usage of MDA, where models are used to drive the software development process. The models capture requirements, design, behavior, and other aspects of the software system, and automated tools transform these models into executable code or other implementation artifacts.

2. Model Driven Systems Engineering: In systems engineering, models are used to represent and analyze the system architecture, behavior, requirements, and other aspects. Model Driven Systems Engineering (MDSE) employs models to capture and manipulate system-related information, aiding in the design, verification, and validation of complex systems.

3. Model Driven Business Process Management: Models are used to represent business processes, workflows, and organizational structures in the context of business process management. Model Driven Business Process Management (MD-BPM) focuses on using models to analyze, optimize, and automate business processes.

4. Model Driven Internet of Things (MD-IoT): In the realm of the Internet of Things (IoT), models can be employed to capture the characteristics, relationships, and behaviors of interconnected devices, sensors, and systems. Model Driven IoT approaches aim to simplify IoT development and management by using models to represent the IoT ecosystem.

These are just a few examples of how the Model Driven Anything concept can be applied in different domains. The underlying principle is that models act as a common language and a central artifact to describe, analyze, and transform complex systems, enabling more efficient development, integration, and management.

The specific techniques, tools, and languages used in Model Driven Anything can vary depending on the domain and context, but they generally involve the creation, manipulation, and transformation of models to support various processes and activities.

Model-Driven Analytics

Around 2010, I wrote a paper on the “Progression of Analytics”. Many were confused on what Analytics was versus Advanced Analytics, and many were confusing my leadership on what Machine Learning was and how to approach this. Many people were stumbling through their understanding of “Big Data” no thanks to talking heads and consulting companies attempting to sell their wares. So in order to gain alignment with those confused people, I published this model and a plethora of content. You can find the gist of that material here, The Premise of Analytics.

Model-Driven Analytics is an approach to data analysis and decision-making that leverages models as the central framework for understanding, predicting, and optimizing business outcomes. It involves using mathematical, statistical, or machine learning models to analyze data, gain insights, make predictions, and drive data-informed decisions.

Key characteristics of Model-Driven Analytics include:

1. Data Modeling: Model-Driven Analytics starts with the creation of data models that represent the structure, relationships, and attributes of the relevant data. These models serve as a foundation for organizing and understanding the data, ensuring its integrity, and enabling efficient analysis.

2. Model Development: Models are developed using various techniques, such as statistical modeling, machine learning, or predictive analytics. These models are trained or calibrated using historical data and can be used to make predictions, classify data, detect anomalies, or uncover patterns and relationships.

3. Data Analysis and Exploration: Model-Driven Analytics involves applying the developed models to analyze data, identify trends, patterns, and correlations, and extract meaningful insights. Exploratory data analysis techniques are used to uncover hidden information and gain a deeper understanding of the data.

4. Predictive Analytics: Model-Driven Analytics aims to predict future outcomes or behaviors based on historical data and the developed models. Predictive models can be used to forecast sales, customer behavior, market trends, equipment failure, or any other relevant aspect, enabling proactive decision-making.

5. Optimization and Decision Support: Models can be used to optimize processes, resources, or strategies. By incorporating models into decision-making processes, organizations can evaluate different scenarios, perform sensitivity analysis, and make informed decisions to maximize desired outcomes.

6. Model Evaluation and Iteration: Model-Driven Analytics involves evaluating the performance and accuracy of the developed models against new data or real-world outcomes. If necessary, models are refined, recalibrated, or replaced to ensure their effectiveness and reliability.

7. Automation and Integration: Model-Driven Analytics often involves automating the data analysis process and integrating models into operational systems or business workflows. This enables real-time or near-real-time decision-making, allowing organizations to respond quickly to changing conditions.

Model-Driven Analytics enables organizations to derive insights, optimize processes, and make data-driven decisions by leveraging the power of models. It enhances the understanding of complex data sets, uncovers relationships, and facilitates predictive capabilities, leading to improved business outcomes and strategic advantages.

Model-Driven Learning

In 2013, at a Conference, I spoke about Model-Driven-Data. Many who knew me, that I was talking about model-driven development, but I was not. Even today, there is not much about Model-Driven Data or Model-Driven Learning. I would say Model-Driven Learning is based on the same premise as Model-Driven Analytics, but extends to Model-Driven Advanced Analytics in the Progression of Analytics (e.g. Descriptive, Diagnostics, Predictive, and Prescriptive).

To me “data” and “intelligence” are the same thing. I have seen some brilliant data scientists use Common Workflow Language (CWL) and TOIL to manage extremely large datasets to build learning-at-scale across various cloud platforms.

As we moved from Supervised Learning to Unsupervised Learning, the need to plan and manage such objectives will require the ability to do Model-Driven Learning, extending the field of Model-Driven Analytics, and through other Model-Driven techniques.

Closing

I will attempt to expose these ideas through this blog and my UML Operator Channel. I will touch on this subject and hope that members will contribute and share other great works. 

So many great thinkers out there! I look forward to following, sharing, and more.

Happy Modeling!

Tim, The UML Operator