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Data modeling techniques and methodologies are used to model data in a standard, consistent, predictable manner in order to manage it as a resource. The use of data modeling standards is strongly recommended for all projects requiring a standard means of defining and analyzing data within an organization, e.g., using data modeling:
The following steps are suggestion of the data modeling process for Microsoft Access, a relational DBMS. Determine the purpose of the database – This helps prepare for the remaining steps. Find and organize the information required – Gather all of the types of information to record in the database, such as product name and order number.
Recording requirements: Requirements may be documented in various forms, usually including a summary list, and may include natural-language documents, use cases, user stories, process specifications, and a variety of models including data models. Analyzing requirements: determining whether the stated requirements are clear, complete ...
Logical Data Modelling The process of identifying, modelling and documenting the data requirements of the system being designed. The result is a data model containing entities (things about which a business needs to record information), attributes (facts about the entities) and relationships (associations between the entities). Data Flow Modelling
Figure demonstrating the concept of anchor modeling. Anchor modeling is an agile database modeling technique suited for information that changes over time both in structure and content. It provides a graphical notation used for conceptual modeling similar to that of entity-relationship modeling, with extensions for working with temporal data.
An alternative to implementing ORM is use of the native procedural languages provided with every major database. These can be called from the client using SQL statements. The Data Access Object (DAO) design pattern is used to abstract these statements and offer a lightweight object-oriented interface to the rest of the application. [5]
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Dynamic user models; Dynamic user models allow a more up to date representation of users. Changes in their interests, their learning progress or interactions with the system are noticed and influence the user models. The models can thus be updated and take the current needs and goals of the users into account. Stereotype based user models