Database Systems: Design, Implementation, and

Management Tenth Edition

Chapter 13 Business Intelligence and Data

Warehouses

Objectives

In this chapter, you will learn: • How business intelligence provides a

comprehensive business decision support framework

• About business intelligence architecture, its evolution, and reporting styles

• About the relationship and differences between operational data and decision support data

• What a data warehouse is and how to prepare data for one

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Objectives (cont’d.)

• What star schemas are and how they are constructed

• About data analytics, data mining, and predictive analytics

• About online analytical processing (OLAP) • How SQL extensions are used to support

OLAP-type data manipulations

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The Need for Data Analysis

• Managers track daily transactions to evaluate how the business is performing

• Strategies should be developed to meet organizational goals using operational databases

• Data analysis provides information about short- term tactical evaluations and strategies

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Business Intelligence

• Comprehensive, cohesive, integrated tools and processes – Capture, collect, integrate, store, and analyze

data

– Generate information to support business decision making

• Framework that allows a business to transform: – Data into information

– Information into knowledge

– Knowledge into wisdom Database Systems, 10th Edition 5

Business Intelligence Architecture

• Composed of data, people, processes, technology, and management of components

• Focuses on strategic and tactical use of information

• Key performance indicators (KPI) – Measurements that assess company’s

effectiveness or success in reaching goals

• Multiple tools from different vendors can be integrated into a single BI framework

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Business Intelligence Benefits

• Main goal: improved decision making • Other benefits

– Integrating architecture

– Common user interface for data reporting and analysis

– Common data repository fosters single version of company data

– Improved organizational performance

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Business Intelligence Evolution

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Business Intelligence Technology Trends

• Data storage improvements • Business intelligence appliances • Business intelligence as a service • Big Data analytics • Personal analytics

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Decision Support Data

• BI effectiveness depends on quality of data gathered at operational level

• Operational data seldom well-suited for decision support tasks

• Need reformat data in order to be useful for business intelligence

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Operational Data vs. Decision Support Data

• Operational data – Mostly stored in relational database – Optimized to support transactions representing

daily operations

• Decision support data differs from operational data in three main areas: – Time span

– Granularity

– Dimensionality

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Decision Support Database Requirements

• Specialized DBMS tailored to provide fast answers to complex queries

• Three main requirements – Database schema

– Data extraction and loading

– Database size

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Decision Support Database Requirements (cont’d.)

• Database schema – Complex data representations – Aggregated and summarized data – Queries extract multidimensional time slices

• Data extraction and filtering – Supports different data sources

• Flat files • Hierarchical, network, and relational databases • Multiple vendors

– Checking for inconsistent data Database Systems, 10th Edition 16

Decision Support Database Requirements (cont’d.)

• Database size – In 2005, Wal-Mart had 260 terabytes of data in

its data warehouses

– DBMS must support very large databases (VLDBs)

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The Data Warehouse

• Integrated, subject-oriented, time-variant, and nonvolatile collection of data – Provides support for decision making

• Usually a read-only database optimized for data analysis and query processing

• Requires time, money, and considerable managerial effort to create

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Data Marts

• Small, single-subject data warehouse subset • More manageable data set than data

warehouse • Provides decision support to small group of

people • Typically lower cost and lower implementation

time than data warehouse

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Twelve Rules That Define a Data Warehouse

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Star Schemas

• Data-modeling technique – Maps multidimensional decision support data

into relational database

• Creates near equivalent of multidimensional database schema from relational data

• Easily implemented model for multidimensional data analysis while preserving relational structures

• Four components: facts, dimensions, attributes, and attribute hierarchies

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Facts

• Numeric measurements that represent specific business aspect or activity – Normally stored in fact table that is center of star

schema

• Fact table contains facts linked through their dimensions

• Metrics are facts computed at run time

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Dimensions

• Qualifying characteristics provide additional perspectives to a given fact

• Decision support data almost always viewed in relation to other data

• Study facts via dimensions • Dimensions stored in dimension tables

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Attributes

• Use to search, filter, and classify facts • Dimensions provide descriptions of facts

through their attributes • No mathematical limit to the number of

dimensions • Slice and dice: focus on slices of the data cube

for more detailed analysis

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Attribute Hierarchies

• Provide top-down data organization • Two purposes:

– Aggregation

– Drill-down/roll-up data analysis

• Determine how the data are extracted and represented

• Stored in the DBMS’s data dictionary • Used by OLAP tool to access warehouse

properly

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Star Schema Representation

• Facts and dimensions represented in physical tables in data warehouse database

• Many fact rows related to each dimension row – Primary key of fact table is a composite primary

key

– Fact table primary key formed by combining foreign keys pointing to dimension tables

• Dimension tables are smaller than fact tables • Each dimension record is related to thousands

of fact records Database Systems, 10th Edition 27

Performance-Improving Techniques for the Star Schema

• Four techniques to optimize data warehouse design: – Normalizing dimensional tables

– Maintaining multiple fact tables to represent different aggregation levels

– Denormalizing fact tables

– Partitioning and replicating tables

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Performance-Improving Techniques for the Star Schema (cont’d.)

• Dimension tables normalized to: – Achieve semantic simplicity – Facilitate end-user navigation through the

dimensions

• Denormalizing fact tables improves data access performance and saves data storage space

• Partitioning splits table into subsets of rows or columns

• Replication makes copy of table and places it in different location

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Data Analytics

• Subset of BI functionality • Encompasses a wide range of mathematical,

statistical, and modeling techniques – Purpose of extracting knowledge from data

• Tools can be grouped into two separate areas: – Explanatory analytics

– Predictive analytics

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Data Mining

• Data-mining tools do the following: – Analyze data – Uncover problems or opportunities hidden in

data relationships

– Form computer models based on their findings – Use models to predict business behavior

• Runs in two modes – Guided

– Automated

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Predictive Analytics

• Employs mathematical and statistical algorithms, neural networks, artificial intelligence, and other advanced modeling tools

• Create actionable predictive models based on available data

• Models are used in areas such as: – Customer relationships, customer service,

customer retention, fraud detection, targeted marketing, and optimized pricing

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Online Analytical Processing

• Three main characteristics: – Multidimensional data analysis techniques – Advanced database support

– Easy-to-use end-user interfaces

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Multidimensional Data Analysis Techniques

• Data are processed and viewed as part of a multidimensional structure

• Augmented by the following functions: – Advanced data presentation functions

– Advanced data aggregation, consolidation, and classification functions

– Advanced computational functions

– Advanced data modeling functions

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Advanced Database Support

• Advanced data access features include: – Access to many different kinds of DBMSs, flat

files, and internal and external data sources

– Access to aggregated data warehouse data

– Advanced data navigation – Rapid and consistent query response times

– Maps end-user requests to appropriate data source and to proper data access language

– Support for very large databases

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Easy-to-Use End-User Interface

• Advanced OLAP features are more useful when access is simple

• Many interface features are “borrowed” from previous generations of data analysis tools – Already familiar to end users

– Makes OLAP easily accepted and readily used

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OLAP Architecture

• Three main architectural components: – Graphical user interface (GUI) – Analytical processing logic

– Data-processing logic

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OLAP Architecture (cont’d.)

• Designed to use both operational and data warehouse data

• In most implementations, data warehouse and OLAP are interrelated and complementary

• OLAP systems merge data warehouse and data mart approaches

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Relational OLAP

• Relational online analytical processing (ROLAP) provides the following extensions: – Multidimensional data schema support within the

RDBMS

– Data access language and query performance optimized for multidimensional data

– Support for very large databases (VLDBs)

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Multidimensional OLAP

• Multidimensional online analytical processing (MOLAP) extends OLAP functionality to multidimensional database management systems (MDBMSs) – MDBMS end users visualize stored data as a 3D

data cube

– Data cubes can grow to n dimensions, becoming hypercubes

– To speed access, data cubes are held in memory in a cube cache

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Relational vs. Multidimensional OLAP

• Selection of one or the other depends on evaluator’s vantage point

• Proper evaluation must include supported hardware, compatibility with DBMS, etc.

• ROLAP and MOLAP vendors working toward integration within unified framework

• Relational databases use star schema design to handle multidimensional data

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SQL Extensions for OLAP

• Proliferation of OLAP tools fostered development of SQL extensions

• Many innovations have become part of standard SQL

• All SQL commands will work in data warehouse as expected

• Most queries include many data groupings and aggregations over multiple columns

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The ROLLUP Extension

• Used with GROUP BY clause to generate aggregates by different dimensions

• GROUP BY generates only one aggregate for each new value combination of attributes

• ROLLUP extension enables subtotal for each column listed except for the last one – Last column gets grand total

• Order of column list important

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The CUBE Extension

• CUBE extension used with GROUP BY clause to generate aggregates by listed columns – Includes the last column

• Enables subtotal for each column in addition to grand total for last column – Useful when you want to compute all possible

subtotals within groupings

• Cross-tabulations are good candidates for application of CUBE extension

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Materialized Views

• A dynamic table that contains SQL query command to generate rows – Also contains the actual rows

• Created the first time query is run and summary rows are stored in table

• Automatically updated when base tables are updated

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Summary

• Business intelligence generates information used to support decision making

• BI covers a range of technologies, applications, and functionalities

• Decision support systems were the precursor of current generation BI systems

• Operational data not suited for decision support

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Summary (cont’d.)

• Data warehouse provides support for decision making – Usually read-only

– Optimized for data analysis, query processing

• Star schema is a data-modeling technique – Maps multidimensional decision support data

into a relational database

• Star schema has four components: – Facts, dimensions, attributes, and attribute

hierarchies Database Systems, 10th Edition 50

Summary (cont’d.)

• Data analytics – Provides advanced data analysis tools to extract

knowledge from business data

• Data mining – Automates the analysis of operational data to

find previously unknown data characteristics, relationships, dependencies, and trends

• Predictive analytics – Uses information generated in the data-mining

phase to create advanced predictive models

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Summary (cont’d.)

• Online analytical processing (OLAP) – Advanced data analysis environment that

supports decision making, business modeling, and operations research

• SQL has been enhanced with extensions that support OLAP-type processing and data generation

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Database Systems: Design, Implementation, and

Management Eighth Edition

Chapter 11 Database Performance Tuning and

Query Optimization

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Objectives

• In this chapter, you will learn: – Basic database performance-tuning concepts – How a DBMS processes SQL queries – About the importance of indexes in query processing – About the types of decisions the query optimizer has

to make – Some common practices used to write efficient SQL

code – How to formulate queries and tune the DBMS for

optimal performance – Performance tuning in SQL Server 2005

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11.1 Database Performance-Tuning Concepts

• Goal of database performance is to execute queries as fast as possible

• Database performance tuning – Set of activities and procedures designed to

reduce response time of database system

• All factors must operate at optimum level with minimal bottlenecks

• Good database performance starts with good database design

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Performance Tuning: Client and Server

• Client side – Generate SQL query that returns correct answer

in least amount of time • Using minimum amount of resources at server

– SQL performance tuning

• Server side – DBMS environment configured to respond to

clients’ requests as fast as possible • Optimum use of existing resources

– DBMS performance tuning

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DBMS Architecture

• All data in database are stored in data files • Data files

– Automatically expand in predefined increments known as extends

– Grouped in file groups or table spaces • Table space or file group:

– Logical grouping of several data files that store data with similar characteristics

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Basic DBMS architecture

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DBMS Architecture (continued)

• Data cache or buffer cache: shared, reserved memory area – Stores most recently accessed data blocks in RAM

• SQL cache or procedure cache: stores most recently executed SQL statements – Also PL/SQL procedures, including triggers and

functions

• DBMS retrieves data from permanent storage and places it in RAM

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DBMS Architecture (continued)

• Input/output request: low-level data access operation to/from computer devices, such as memory, hard disks, videos, and printers

• Data cache is faster than data in data files – DBMS does not wait for hard disk to retrieve data

• Majority of performance-tuning activities focus on minimizing I/O operations

• Typical DBMS processes: – Listener, User, Scheduler, Lock manager, Optimizer

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Database Statistics

• Measurements about database objects and available resources – Tables, Indexes, Number of processors used,

Processor speed, Temporary space available • Make critical decisions about improving query

processing efficiency • Can be gathered manually by DBA or automatically by

DBMS – UPDATE STATISTICS table_name [index_name] – Auto-Update and Auto-Create Statistics option

• 資料庫屬性 -> 自動更新統計資料 • 資料庫屬性 -> 自動建立統計資料

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Ch08: dbcc show_statistics (customer, PK__CUSTOMER__24927208 )

Ch08: dbcc show_statistics (customer, CUS_UI1)

補充 SQL Server 2005

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11.2 Query Processing

• DBMS processes queries in three phases – Parsing

• DBMS parses the query and chooses the most efficient access/execution plan

– Execution • DBMS executes the query using chosen

execution plan

– Fetching • DBMS fetches the data and sends the result back

to the client

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SQL Parsing Phase

• Break down query into smaller units • Transform original SQL query into slightly

different version of original SQL code – Fully equivalent

• Optimized query results are always the same as original query

– More efficient • Optimized query will almost always execute faster

than original query

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SQL Parsing Phase (continued) • Query optimizer analyzes SQL query and finds most

efficient way to access data – Validated for syntax compliance

– Validated against data dictionary • Tables, column names are correct • User has proper access rights

– Analyzed and decomposed into more atomic components

– Optimized through transforming into a fully equivalent but more efficient SQL query

– Prepared for execution by determining the execution or access plan

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SQL Parsing Phase (continued)

• Access plans are DBMS-specific – Translate client’s SQL query into series of

complex I/O operations – Required to read the data from the physical data

files and generate result set • DBMS checks if access plan already exists for

query in SQL cache • DBMS reuses the access plan to save time • If not, optimizer evaluates various plans

– Chosen plan placed in SQL cache

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SQL Execution and Fetching Phase

• All I/O operations indicated in access plan are executed – Locks acquired – Data retrieved and placed in data cache – Transaction management commands processed

• Rows of resulting query result set are returned to client

• DBMS may use temporary table space to store temporary data – The server may send only the first 100 rows of 9000 rows

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Query Processing Bottlenecks

• Delay introduced in the processing of an I/O operation that slows the system – CPU

– RAM

– Hard disk

– Network

– Application code

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SQL 敘述 輸入完成 後先不要 執行查 詢 , 請按 下工具列 的顯示估 計執行計

劃鈕 ：

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11.3 Indexes and Query Optimization

• Indexes – Crucial in speeding up data access

– Facilitate searching, sorting, and using aggregate functions as well as join operations

– Ordered set of values that contains index key and pointers

• More efficient to use index to access table than to scan all rows in table sequentially

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Indexes and Query Optimization • Data sparsity: number of different values a column

could possibly have • Indexes implemented using: ( 課本 p. 453)

– Hash indexes

– B-tree indexes: most common index type. Used in tables in which column values repeat a small number of times. The leaves contain pointers to records It is self-balanced.

– Bitmap indexes: 0/1

• DBMSs determine best type of index to use – Ex: CUST_LNAME with B-tree and REGION_CODE with

Bitmap indexes

Database Systems, 8th Edition 24B-tree and bitmap index representation

25

Index Representation for the CUSTOMER table

SELECT CUS_NAME FROM CUSTOMER WHERE CUS_STATE=‘FL’ Requires only 5 accesses to STATE_INDEX, 5 accesses to CUSTOMER

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11.4 Optimizer Choices

• Rule-based optimizer – Preset rules and points

– Rules assign a fixed cost to each operation

• Cost-based optimizer – Algorithms based on statistics about objects

being accessed

– Adds up processing cost, I/O costs, resource costs to derive total cost

Example

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SELECT P_CODE, P_DESCRIPT, P_PRICE, V_NAME, V_STATE FROM PRODUCT P, VENDOR V WHERE P.V_CODE=V.V_CODE AND V.V_STATE=‘FL’;

• With the following database statistics: – The PRODUCT table has 7000 rows – The VENDOR table has 300 rows – 10 vendors come from Florida – 1000 products come from vendors in Florida

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Example

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• Assume the PRODUCT table has the index PQOH_NDX in the P_QOH attribute

SELECT MIN(P_QOH) FROM PRODUCT

could be resolved by reading only the first entry in the PQOH_NDX index

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Using Hints to Affect Optimizer Choices

• Optimizer might not choose best plan • Makes decisions based on existing statistics

– Statistics may be old – Might choose less efficient decisions

• Optimizer hints: special instructions for the optimizer embedded in the SQL command text

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Oracle 版本

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MS SQL Server 的語法請參考：

http://msdn.microsoft.com/en-us/library/

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