OIM 350 Exam 3

Relational online analytical processing

An OLAP architecture that stores data in relational tables.

Drill up

An OLAP operation for making the granularity of the data in the query result coarser.

Pivot (rotate)

An OLAP operation that reorganizes the values displayed in the original query result by moving values of a dimension column from one axis to another.

Aggregated Fact Tables

Fact tables in which each record summarizes multiple facts.

Time Series

Graphs where the X axis is time and measures change(s) over that time

Data Ink

Ink that is used to convey data and does not include anything that's not directly representing data, which includes: labels, axes, gridlines, scales, legends

Fact tables

Tables in a dimensional model that contain measures related to the subject of analysis and foreign keys that connect the fact table to the dimension tables.

Type 1 approach

The approach to handling slowly changing dimensions based on overwriting values in records of dimensions; it is used mostly when a change in a dimension is the result of an error.

Duck Charts

excessive ornamentation, non-useful 3D, low data-ink ratios (or redundant data-ink)

Transaction time

A column representing the time of the transaction.

Timestamps

Columns in tables that indicate the time interval for which the values in the records are applicable.

Aggregated Data

Data representing summarization of multiple instances of data.

Dimensionally modeled data warehouse

Data warehouse modeled using dimensional modeling.

Active data warehouses

Data warehouses with loads that occur continuously in micro batches, ensuring that the data in the data warehouse is updated close to real time (enabling analysis of the latest data).

Granularity

Describes what is depicted by one row in the fact table

Refresh cycle

The frequency with which the data warehouse is reloaded with new data.

Beta Release

The system release (following the alpha releases), in which the system is deployed to a selected group of users to test the usability of the system.

Online transaction processing

Updating (i.e., inserting, modifying, and deleting), querying, and presenting data from databases for operational purposes.

Faceting

Using several distinct graphs arranged in a manner for comparison in order to add dimensionality

Multidimensional database model

A model for implementation of dimensionally modeled data in which the database is implemented as a collection of cubes.

Transaction identifier

A column representing the transaction id.

Row indicator

A column that provides a quick indicator of whether the record is currently valid.

Dimensional modeling

A data design methodology used for designing subject-oriented analytical databases, i.e., data warehouses or data marts.

Normalized data warehouse

A data warehouse modeled by using the traditional database modeling techniques of ER modeling, relational modeling, and/or normalization, resulting in a normalized set of tables.

Slowly changing dimensions

A dimension that contains attributes whose values can change.

Line-item detailed fact table

A fact table in which each record represents a line item of a particular transaction.

Transaction-level detailed fact table

A fact table in which each row represents a particular transaction.

Hybrid online analytical processing

An OLAP architecture that combines MOLAP and ROLAP approaches.

Multidimensional line analytical processing

An OLAP architecture that stores data in multidimensional cubes.

Slice and dice

An OLAP operation that adds, replaces, or eliminates specified dimension attributes (or particular values of the dimension attributes) from the already displayed result.

Type 2 approach

An approach to handling slowly changing dimensions that is used in cases when history should be preserved; it creates a new additional dimension record using a new value for the surrogate key every time a value in a dimension record changes.

Type 3 approach

An approach to handling slowly changing dimensions used in cases when there is a fixed number of changes possible per column of a dimension, or in cases when only a limited history is recorded; it creates a "previous" and "current" column in the dimension table, for each column where the changes are anticipated.

OLAP/BI Tools

see online analytical processing tools

Bivariate Graphs

"Two variables" x and y. Typically scatterplots (dots at each observation). Useful for examining one-to-one relationships (correlations)

Executive Dashboard

A front-end application that contains an organized, easy-to-read display of critically important queries describing the performance of the organization.

Surrogate Key

A noncomposite system-generated key assigned to each dimension of a star schema.

Snowflake models

A star schema that contains the dimensions that are normalized.

Star Schema

Schema containing fact tables and dimensions.

Online analytical processing (OLAP) tools

Tools enabling end users to engage in an ad-hoc analytical querying of data warehouses.

Business Intelligence Tools

Tools that can be used to perform data analysis and create sophisticated charts and reports.

Drill down

An OLAP operation for making the granularity of the data in the query result finer.

Degenerate Dimension

An event identifier included within the fact table (rather than having its own separate dimension).

Spatial Graphs (maps)

Applying an overlay of data visualization where the location in space is important

Conformed Dimensions

Standardized dimensions created before development of star schemas; typically used with multiple fact tables.

Constellation of Stars

Star schema containing multiple fact tables.

Moiré Effects (visual vibrations)

Static images appear to move (vibrate) when lines are too close to each other to overlap. This draws the viewers attention to the vibration effects rather than to the actual data being displayed.

Alpha release

Internal deployment of a system to the members of the development team for initial testing of its functionalities.

Online analytical processing (OLAP)

Querying and present-ing data from data warehouses and/or data marts for analytical purposes.

Data cleansing (scrubbing)

The detection and correction of low-quality data.

First Load

The initial data warehouse load populating the empty data warehouse.

Refresh Load

Every subsequent load after the first load.

Cubes

Multidimensional database repositories; can be used to store dimensionally modeled data.

Production Release

The actual deployment of a functioning system.