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Concept
Anchor modeling
Summary
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. The modeling technique involves four modeling constructs: the anchor, attribute, tie and knot, each capturing different aspects of the domain being modeled. The resulting models can be translated to physical database designs using formalized rules. When such a translation is done the tables in the relational database will mostly be in the sixth normal form.
Unlike the star schema (dimensional modelling) and the classical relational model (3NF), data vault and anchor modelling are well-suited for capturing changes that occur when a source system is changed or added, but are considered advanced techniques which require experienced data architects. Both data vaults and anchor models are entity-based models, but anchor models have a more normalized approach.
Anchor modeling was created in order to take advantage of the benefits from a high degree of normalization while avoiding its drawbacks which higher normal forms have with regards to human readability. Advantages such as being able to non-destructively evolve the model, avoid null values, and keep the information free from redundancies are gained. Performance issues due to extra joins are largely avoided thanks to a feature in modern database engines called join elimination or table elimination. In order to handle changes in the information content, anchor modeling emulates aspects of a temporal database in the resulting relational database schema.
The earliest installations using anchor modeling were made 2004 in Sweden when a data warehouse for an insurance company was built using the technique.
In 2007 the technique was being used in a few data warehouses and one online transaction processing (OLTP) system, and it was presented internationally by Lars Rönnbäck at the 2007 Transforming Data with Intelligence (TDWI) conference in Amsterdam.
Official source
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