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In software engineering, domain analysis, or product line analysis, is the process of analyzing related software systems in a domain to find their common and variable parts. It is a model of wider business context for the system. The term was coined in the early 1980s by James Neighbors. [1] [2] Domain analysis is the first phase of domain ...
Domain analysis is used to define the domain, collect information about the domain, and produce a domain model. [11] Through the use of feature models (initially conceived as part of the feature-oriented domain analysis method), domain analysis aims to identify the common points in a domain and the varying points in the domain. [12]
The intent of feature-oriented domain analysis is to support functional and architectural reuse. The objective is to create a domain model which represents a family of systems which can then be refined into the particular desired system within the domain [6] To do this, the scope of the domain must be analyzed (known as FODA context analysis) to identify not only the systems in the domain but ...
In complex analysis, a complex domain (or simply domain) is any connected open subset of the complex plane C. For example, the entire complex plane is a domain, as is the open unit disk, the open upper half-plane, and so forth. Often, a complex domain serves as the domain of definition for a holomorphic function.
The exact data collection procedure is dependent on the domain in question and the availability of data. In most cases, the procedure commences with some form of document analysis. Document analysis allows the analyst to gain a basic domain understanding, forming the basis for semi-structured interviews with domain experts.
Spectrum analysis, also referred to as frequency domain analysis or spectral density estimation, is the technical process of decomposing a complex signal into simpler parts. As described above, many physical processes are best described as a sum of many individual frequency components.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Statistics is the mathematical science involving the collection, analysis and interpretation of data. A number of specialties have evolved to apply statistical and methods to various disciplines. Certain topics have "statistical" in their name but relate to manipulations of probability distributions rather than to statistical analysis.