8-1數(shù)據(jù)倉庫與數(shù)據(jù)挖掘(已修改)
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【正文】 Chapter 20: Data Analysis 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Chapter 20: Data Analysis ? Decision Support Systems ? Data Warehousing ? Data Mining ? Classification ? Association Rules ? Clustering 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Decision Support Systems ? Decisionsupport systems are used to make business decisions, often based on data collected by online transactionprocessing systems. ? Examples of business decisions: ? What items to stock? ? What insurance premium to change? ? To whom to send advertisements? ? Examples of data used for making decisions ? Retail sales transaction details ? Customer profiles (ine, age, gender, etc.) 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition DecisionSupport Systems: Overview ? Data analysis tasks are simplified by specialized tools and SQL extensions ? Example tasks ? For each product category and each region, what were the total sales in the last quarter and how do they pare with the same quarter last year ? As above, for each product category and each customer category ? Statistical analysis packages (., : S++) can be interfaced with databases ? Statistical analysis is a large field, but not covered here ? Data mining seeks to discover knowledge automatically in the form of statistical rules and patterns from large databases. ? A data warehouse archives information gathered from multiple sources, and stores it under a unified schema, at a single site. ? Important for large businesses that generate data from multiple divisions, possibly at multiple sites ? Data may also be purchased externally 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Data Warehousing ? Data sources often store only current data, not historical data ? Corporate decision making requires a unified view of all anizational data, including historical data ? A data warehouse is a repository (archive) of information gathered from multiple sources, stored under a unified schema, at a single site ? Greatly simplifies querying, permits study of historical trends ? Shifts decision support query load away from transaction processing systems 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Data Warehousing 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Design Issues ? When and how to gather data ? Source driven architecture: data sources transmit new information to warehouse, either continuously or periodically (., at night) ? Destination driven architecture: warehouse periodically requests new information from data sources ? Keeping warehouse exactly synchronized with data sources (., using twophase mit) is too expensive ? Usually OK to have slightly outofdate data at warehouse ? Data/updates are periodically downloaded form online transaction processing (OLTP) systems. ? What schema to use ? Schema integration 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition More Warehouse Design Issues ? Data cleansing ? ., correct mistakes in addresses (misspellings, zip code errors) ? Merge address lists from different sources and purge duplicates ? How to propagate updates ? Warehouse schema may be a (materialized) view of schema from data sources ? What data to summarize ? Raw data may be too large to store online ? Aggregate values (totals/subtotals) often suffice ? Queries on raw data can often be transformed by query optimizer to use aggregate values 169。Silberschatz, Korth and Sudarshan Database System Concepts 6th Edition Warehouse Schemas ? Dimension values are usually encoded using small integers and mapped to full values via dimension tables ? Resultant schema is called a star schema ? M
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