数据挖掘_datamiming_韩家炜_04OLAP

发布时间：2021-06-05

Data Mining:Concepts and Techniques(3rd ed.)

— Chapter 4 —Jiawei Han, Micheline Kamber, and Jian PeiUniversity of Illinois at Urbana-Champaign & Simon Fraser University

Chapter 4: Data Warehousing and On-line Analytical Processing

Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Summary3

What is a Data Warehouse?

Defined in many different ways, but not rigorously.

A decision support database that is maintained separately fromthe organization’s operational database Support information processing by providing a solid platform of

consolidated, historical data for analysis.

“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s

decision-making process.”—W. H. Inmon

Data warehousing:

The process of constructing and using data warehouses

Data Warehouse—Subject-Oriented

Organized around major subjects, such as customer, product, sales Focusing on the modeling and analysis of data for

decision makers, not on daily operations or transactionprocessing

Provide a simple and concise view around particular

subject issues by excluding data that are not useful inthe decision support process

Data Warehouse—Integrated

Constructed by integrating multiple, heterogeneous data sources relational databases, flat files, on-line transaction records Data cleaning and data integration techniques are applied. Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

E.g., Hotel price: currency, tax, breakfast covered, etc.

When data is moved to the warehouse, it is converted.6

Data Warehouse—Time Variant

The time horizon for the data warehouse is significantly longer than that of operational systems

Operational database: current value data Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) Contains an element of time, explicitly or implicitly But the key of operational data may or may not contain “time element”

Every key structure in the data warehouse

Data Warehouse—Nonvolatile

A physically separate store of data transformed from the

operational environment

Operational update of data does not occur in the data warehouse environment

Does not require transaction processing, recovery, and concurrency control mechanisms

Requires only two operations in data accessing:

initial loading of data and access of data

OLTP vs. OLAPOLTP users function DB design data usage access unit of work # records accessed #users DB size metric clerk, IT professional day to day operations application-oriented current, up-to-date detailed, flat relational isolated repetiti

ve read/write index/hash on prim. key short, simple transaction tens thousands 100MB-GB transaction throughput OLAP knowledge worker decision support subject-oriented historical, summarized, multidimensional integrated, consolidated ad-hoc lots of scans complex query millions hundreds 100GB-TB query throughput, response

Why a Separate Data Warehouse?

High performance for both systems

DBMS— tuned for OLTP: access methods, indexing, concurrency control, recoveryWarehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation missing data: Decision support requires historical data which operational DBs do not typically maintain data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled

Different functions and different data:

Note: There are more and more systems which perform OLAP analysis directly on relational databases10

Data Warehouse: A Multi-Tiered ArchitectureMonitor & Integrator

Other sources

Metadata

OLAP Server

Operational DBs

Extract Transform Load Refresh

Data Warehouse

Serve

Analysis Query Reports Data mining

Data Marts

Data Sources

Data Storage

OLAP Engine Front-End Tools11

Three Data Warehouse Models

Enterprise warehouse collects all of the information about subjects spanning the entire organization Data Mart a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart

Independent vs. dependent (directly from warehouse) data mart

Virtual warehouse A set of views over operational databases Only some of the possible summary views may be materialized12

Extraction, Transformation, and Loading (ETL)

Data extraction get data from multiple, heterogeneous, and external sources Data cleaning detect errors in the data and rectify them when possible Data transformation convert data from legacy or host format to warehouse format Load sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions Refresh propagate the updates from the data sources to the warehouse13

Metadata Repository

Meta data is the data defining warehouse objects. It stores:

Description of the structure of the data warehouse

schema, view, dimensions, hierarchies, derived data defn, data mart locations and contents

Operational meta-data

data lineage (history of migrated data and transformation path), currency of data (active, archived, or purged), monitoring information (warehouse usage statistics, error reports, audit trails)

The algorithms used for summarization The mapping from operational environment to the data warehouse Data related to system performance

warehouse schema, view and derived data definitions

Business data

business terms and definitions, ownership of data, charging policies14

Chapter 4: Data Warehousing and On-line Analytical Processing

Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse Design and Usage Data Warehouse Implementation Summary15

From Tables and Spreadsheets to Data Cubes

A data warehouse is based on a multidimensional data model

which views data in the form of a data cube

A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions

Dimension tables, such as item (item_name, brand, type), ortime(day, week, month, quarter, year) Fact table contains measures (such as dollars_sold) and keys

to each of the related dimension tables

In data warehousing literature, an n-D base cube is called a base cuboid. The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid. The lattice of cuboids forms a data cube.16

Cube: A Lattice of Cuboidsall time item location supplier

0-D (apex) cuboid

1-D cuboids

time,location time,item

item,location item,supplier

location,supplier

time,supplier

2-D cuboids

time,location,supplier

3-D cuboids

time,item,location

time,item,supplier

item,location,supplier

4-D (base) cuboidtime, item, location, supplier17

Conceptual Modeling of Data Warehouses

Modeling data warehouses: dimensions & measures

Star schema: A fact table in the middle connected to aset of dimension tables Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars,

therefore called galaxy schema or fact constellation18

Example of Star Schematimetime_key day day_of_the_week month quarter year

itemSales Fact Table time_keyitem_key item_name brand type supplier_type

item_key branch_keylocation_key units_sold dollars_sold avg_sales Measures

branchbranch_key branch_name branch_type

locationlocation_key street city state_or_province country

Example of Snowflake Schematimetime_key day day_of_the_week month quarter year

itemSales Fact Table time_key item_key branch_keyitem_key item_name brand type supplier_key

suppliersupplier_key supplier_type

branchbranch_key branch_name branch_type

location_key units_sold dollars_sold avg_sales

locationlocation_key street city_key