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Database model for MediaWiki 1.28.0 (2017) Different types of database models. A database model is a type of data model that determines the logical structure of a database. It fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a ...
The chain-linked model or Kline model of innovation was introduced by mechanical engineer Stephen J. Kline in 1985, [1] and further described by Kline and economist Nathan Rosenberg in 1986. [2] The chain-linked model is an attempt to describe complexities in the innovation process. The model is regarded as Kline's most significant contribution ...
An industry standard data model, or simply standard data model, is a data model that is widely used in a particular industry. The use of standard data models makes the exchange of information easier and faster because it allows heterogeneous organizations to share an agreed vocabulary, semantics, format, and quality standard for data.
A meta model is a model of the constructs of a modeling system. Like any model, it is used to represent and reason about the subject of the model - in this case IDEF1X. The meta model is used to reason about IDEF1X, i.e., what the constructs of IDEF1X are and how they relate to one another. The model shown is an IDEF1X model of IDEF1X.
The outer circle in the diagram symbolizes the cyclic nature of data mining itself. A data mining process continues after a solution has been deployed. The lessons learned during the process can trigger new, often more focused business questions, and subsequent data mining processes will benefit from the experiences of previous ones.
A life-cycle "model" is sometimes considered a more general term for a category of methodologies and a software development "process" is a particular instance as adopted by a specific organization. [ citation needed ] For example, many specific software development processes fit the spiral life-cycle model.
A real-life ETL cycle may consist of additional execution steps, for example: Cycle initiation; Build reference data; Extract (from sources) Validate; Transform (clean, apply business rules, check for data integrity, create aggregates or disaggregates) Stage (load into staging tables, if used) Audit reports (for example, on compliance with ...
A systems development life cycle is composed of distinct work phases that are used by systems engineers and systems developers to deliver information systems.Like anything that is manufactured on an assembly line, an SDLC aims to produce high-quality systems that meet or exceed expectations, based on requirements, by delivering systems within scheduled time frames and cost estimates. [3]