BBIS304: Business Intelligence and Data Warehousing Lecture Notes

These flashcards cover key terms and concepts related to Business Intelligence and Data Warehousing, useful for exam review.

Business Intelligence (BI)

An umbrella term for applications, infrastructure, tools, and best practices that enable access to information and analysis to improve decisions and performance.

Data Warehouse (DW)

A subject-oriented, integrated, time-variant, and non-volatile collection of data for support in decision-making.

Data Silos

Isolated pockets of data that do not communicate with each other.

Operational Processing

OLTP (Online Transaction Processing) optimized for fast writes, such as recording a sale immediately.

Analytical Processing

OLAP (Online Analytical Processing) optimized for complex reads, such as total sales over multiple years.

Data Mining

The computational process of discovering patterns in large data sets using methods at the intersection of machine learning, statistics, and databases.

ETL (Extract, Transform, Load)

The process of collecting data from various sources, cleaning and reformatting it, and then loading it into a data warehouse.

OLAP Cube

A multidimensional structure that contains data for business measures like Sales or Revenue.

Star Schema

A data modeling technique where one large fact table connects to multiple dimension tables denormalized for speed.

Snowflake Schema

A variation of the Star Schema where dimension tables are normalized, splitting data into additional related tables.

Slowly Changing Dimensions (SCD) Type 2

A method to keep historical data by creating a new row for each change.

Anomaly Detection

Identifying data points that do not fit the usual pattern.

Association Rule Learning

Discovering rules that describe relationships in data.

Clustering

Grouping sets of objects so that those in the same group are more similar to each other than to those in other groups.

Roll-up Operation

Moving from detailed data to a higher summary level in OLAP.

Drill-down Operation

The opposite of roll-up; moving from summary data to more detailed levels in OLAP.

Slice and Dice

Operations in OLAP for viewing specific dimensions or ranges of data.