Normalization is a process that applies a series of rules to a logical data model to determine how well the model is formed.

Denormalization of a data model reduces the number of joins that must be performed in a query, which increases the speed of data access.

IBM DB2, Jasmine, and Oracle are examples of end-user database management systems that can manage huge volumes of data and support applications that run an entire company.

A file contains an electronic list of information that is formatted for a particular transaction, and the information is changed and manipulated by programs that are written for those purposes.

Look-up files store core information that is important to the business and to the application, and are usually kept for long periods of time with new records appended to the end of the file.

The most efficient tables in a relational database in terms of storage space have no redundant data and very few null values because the presence of these suggest that space is being wasted, and more data to store means higher data storage hardware costs.

Optimization is a process whereby a series of rules are applied to a logical data model to determine how well-formed it is; these rules help analysts identify entities that are not represented correctly.

There are several techniques that the project team can use to try to speed up access to the data: denormalization, clustering, and indexing.

Null values in a database are easy to interpret.

Clustering similar records together is one way of reducing access time.