DBMS Chapter 9

A corporate information factory (CIF) is a comprehensive view of organizational data in support of all user data requirements.

True

A dependent data mart is filled from the enterprise data warehouse and its reconciled data.

True

Advances in computer hardware, particularly the emergence of affordable mass storage and parallel computer architectures, was one of the key advances that led to the emergence of data warehousing.

True

An enterprise data warehouse is the control point and single source of all data made available to end users for decision support applications.

True

An enterprise data warehouse that accepts near-real time feeds of transactional data and immediately transforms and loads the appropriate data is called a real-time data warehouse.

True

An event is a database action that results from a transaction.

True

An independent data mart is filled with data extracted from the operational environment without the benefit of a data warehouse.

True

An operational data store is typically a relational database and normalized, but it is tuned for decision-making applications.

True

Drill-down involves analyzing a given set of data at a finer level of detail.

True

For performance reasons, it may be necessary to define more than one fact table for a star schema.

True

Grain and duration have a direct impact on the size of fact tables.

True

Informational systems are designed to support decision making based on historical point-in-time and prediction data.

True

Medical claims and pharmaceutical data would be an example of big data.

True

Organizations adopt data mart architectures because it is easier to have separate, small data warehouses than to get all organizational parties to agree to one view of the organization in a central data warehouse.

True

Periodic data are data that are never physically altered or deleted once they have been added to the store.

True

Rule discovery searches for patterns and correlations in large data sets.

True

Scalable technology is critical to a data mart.

True

The enterprise data model controls the phased evolution of the data warehouse.

True

The first requirement for building a user-friendly interface is a set of metadata that describes the data in the data mart in business terms that users can easily understand.

True

The need for data warehousing in an organization is driven by its need for an integrated view of high-quality data.

True

There are applications for fact tables without any nonkey data, only the foreign keys for the associated dimensions.

True

When a dimension participates in a hierarchy, the database designer can normalize the dimension into a nested set of tables with 1:M relationships between them.

True

A conformed dimension is one or more dimension tables associated with only one fact table.

False

A data mart is a data warehouse that contains data that can be used across the entire organization.

False

A fact table holds descriptive data about the business.

False

A separate data warehouse causes more contention for resources in an organization.

False

A snowflake schema is usually heavily aggregated.

False

An operational data store (ODS) is not designed for use by operational users.

False

An operational data store typically holds a history of snapshots of the state of an organization whereas an enterprise data warehouse does not typically contain history.

False

Independent data marts do not generally lead to redundant data and efforts.

False

Logical data marts are physically separate databases from the enterprise data warehouse.

False

Multidimensional OLAP (MOLAP) tools use variations of SQL and view the database as a relational database, in either a star schema or other normalized or denormalized set of tables.

False

NoSQL is a great technology for storing well-structured data.

False

Operational metadata are derived from the enterprise data model.

False

Reconciled data are data that have been selected, formatted, and aggregated for end-user decision support applications.

False

The development of the relational data model did not contribute to the emergence of data warehousing.

False

The grain of a data warehouse indicates the size and depth of the records.

False

The representation of data in a graphical format is called data mining.

False

The status of data is the representation of the data after an event has occurred.

False

Transient data are never changed.

False

When multiple systems in an organization are synchronized, the need for data warehousing increases.

False