Exam 13: Business Intelligence and Data Warehouses

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Business intelligence is a framework that allows a business to transform data into information, information into knowledge, and knowledge into wisdom.

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_____ are qualifying characteristics that provide additional perspectives to a given fact.

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The basic star schema has four components: facts, , attributes, and attribute hierarchies.

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A schema is a type of star schema in which dimension tables can have their own dimension tables.

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can serve as a test vehicle for companies exploring the potential benefits of data warehouses.

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functionality ranges from simple data gathering and transformation to very complex data analysis and presentation.

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In a star schema, attributes are often used to search, filter, or classify .

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OLAP systems are designed to use both operational and data _____ data.

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A data _____ is a small, single-subject data warehouse subset that provides decision support to a small group of people.

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From a data analyst's point of view, decision support data differ from operational data in three main areas: time span, granularity, and .

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A data warehouse designer must define common business dimensions that will be used by a data analyst to narrow a search, group information, or describe attributes.

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In a typical star schema, each dimension record is related to thousands of records.

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The schema must support complex (non-normalized) data representations.

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Data warehouse data are organized and summarized by table, such as CUSTOMER and ADDRESS.

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An multidimensional database management systems (MDBMS) uses proprietary techniques to store data in ?n-dimensional arrays.

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_____ provide a unified, single point of entry for information distribution.

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A index is based on 0 and 1 bits to represent a given condition.

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ROLAP and MOLAP vendors are working toward the integration of their respective solutions within a unified decision support framework.

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Business intelligence tools use the data warehouse data as the raw materials for data analytics to generate business knowledge.

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The data warehouse development life cycle differs from classical systems development.

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