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i* (pronounced "i star") or i* framework is a modeling language suitable for an early phase of system modeling in order to understand the problem domain. i* modeling language allows to model both as-is and to-be situations. The name i* refers to the notion of distributed intentionality which underlines the framework.
Structured analysis and design technique (SADT) is a systems engineering and software engineering methodology for describing systems as a hierarchy of functions. SADT is a structured analysis modelling language, which uses two types of diagrams: activity models and data models.
The data models should ideally be stored in a repository so that they can be retrieved, expanded, and edited over time. Whitten et al. (2004) determined two types of data modeling: [4] Strategic data modeling: This is part of the creation of an information systems strategy, which defines an overall vision and architecture for information systems.
CMMI was developed by a group from industry, government, and the Software Engineering Institute (SEI) at CMU. CMMI models provide guidance for developing or improving processes that meet the business goals of an organization. A CMMI model may also be used as a framework for appraising the process maturity of the organization. [3]
Scrum is an agile team collaboration framework commonly used in software development and other industries. Scrum prescribes for teams to break work into goals to be completed within time-boxed iterations, called sprints. Each sprint is no longer than one month and commonly lasts two weeks.
V-Model (software development) - an extension of the waterfall model; Unified Process (UP) is an iterative software development methodology framework, based on Unified Modeling Language (UML). UP organizes the development of software into four phases, each consisting of one or more executable iterations of the software at that stage of ...
Strategic design is the application of future-oriented design principles in order to increase an organization's innovative and competitive qualities. Its foundations lie in the analysis of external and internal trends and data, which enables design decisions to be made on the basis of facts rather than aesthetics or intuition.
Model Selection and Training: Choose appropriate algorithms (e.g., linear regression, decision trees, neural networks) and train models using frameworks like TensorFlow or PyTorch. Deployment and Serving: Deploy trained models to production environments using scalable architectures such as containerized services (e.g., Docker and Kubernetes). [11]