Lecture 2 Quiz Flashcards
emerged during the 1990s to support data analyses, rather than performing and recording on-line transactions
Multidimensional data models
3 Important Application Areas of multidimensional data models
data warehousing, on-line analytical processing, data minkng
a large repository of integrated data obtained from several sources in an enterprise for the specific purpose of data analysis
data warehouse
performing queries that aggregate large amounts of detailed or granular data to find overall trends
on-line analytical processing
semi-automatically discover unknown knowledge in large databases, often with multidimensional data
data mining
a multidimensional data structure for capturing and analyzing data; can support multiple dimensions and hierarchies
cube
help provide as much context to the data as possible; are used for selection and grouping of data at a desired level of detail
Dimensions
objects that represent the subjects of the desired analyses or a business measure
Facts
other types of facts
measureless facts, state facts
3 relational representations of multidimensional models
star schema, snowflake schema, fact constellation
a fact table surrounded by a set of dimension tables
star schema
[T or F] Each row in the fact table is a measure while each row in the dimension table is an attribute of the dimension.
T
a refinement of star schema where some dimensions are normalized to avoid redundancy
snowflake schema
multiple fact tables sharing common or conformed dimension tables
fact constellation
a collection of related cubes
data warehouse
a subject-oriented, integrated, time variant, and non-volatile collection of data in support of management’s decision making process
data warehouse
organized around major subjects, such as customer, product, and sales, that concern the business to allow easy analysis for them
subject-oriented
constructed by integrating multiple, heterogeneous data sources
integrated
the process of extracting data from different source systems, and transforming the data into an integrated format, and loading the data into the data warehouse
Extract-Transform-Load
specifying exactly what an individual fact table row represents
declaring the grain
answers the question “what are we measuring in this process?”
identifying facts
