Data Warehousing and Decision Support courtesy of Jiawei Han, Larry Kerschberg, and etc. for some slides. Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY

Data Warehousing and Decision Support

courtesy of Jiawei Han, Larry Kerschberg, and etc.

for some slides.

Jianlin FengSchool of SoftwareSUN YAT-SEN UNIVERSITY

April 19, 2023Data Mining: Concepts and

Techniques 2

Outline

Data Warehouse: Basic Concepts

Data Warehouse Modeling: Data Cube and

OLAP

Summary


Techniques 3

What is a Data Warehouse?

Defined in many different ways, but not rigorously.

A decision support database that is maintained separately from

the 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


Techniques 4

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 transaction

processing

Provide a simple and concise view around particular

subject issues by excluding data that are not useful in the

decision support process


Techniques 5

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.


Techniques 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)

Every key structure in the data warehouse

Contains an element of time, explicitly or implicitly

But the key of operational data may or may not contain

“time element”


Techniques 7

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


Techniques 8

OLTP vs. OLAP OLTP OLAP

users clerk, IT professional knowledge worker

function day to day operations decision support

DB design application-oriented subject-oriented

data current, up-to-date detailed, flat relational isolated

historical, summarized, multidimensional integrated, consolidated

usage repetitive ad-hoc

access read/write index/hash on prim. key

lots of scans

unit of work short, simple transaction complex query

# records accessed tens millions

#users thousands hundreds

DB size 100MB-GB 100GB-TB

metric transaction throughput query throughput, response


Techniques 9

Why a Separate Data Warehouse? High performance for both systems

DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery

Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation

Different functions and different data: 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 Note: There are more and more systems which perform OLAP analysis

directly on relational databases


Techniques 10

Data Warehouse: A Multi-Tiered ArchitectureData Warehouse: A Multi-Tiered Architecture

DataWarehouse

ExtractTransformLoadRefresh

OLAP Engine

AnalysisQueryReportsData mining

Monitor&

Integrator

Metadata

Data Sources Front-End Tools

Serve

Data Marts

Operational DBs

Othersources

Data Storage

OLAP Server


Techniques 11

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

warehouse


Techniques 12

Outline

Data Warehouse: Basic Concepts

Data Warehouse Modeling: Data Cube and

OLAP

Data Warehouse Design and Usage

Data Warehouse Implementation

Summary

Data Cube Concept

Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab and Sub-Totals By Jim Gray et al, in IEEE ICDE’1996

CUBE: A Relational Aggregate Operator Generalizing Group By (from Gray, et al)CUBE: A Relational Aggregate Operator Generalizing Group By (from Gray, et al)

CHEVY

FORD 19901991

19921993

REDWHITEBLUE

By Color

By Make & Color

By Make & Year

By Color & Year

By MakeBy Year

Sum

The Data Cube and The Sub-Space Aggregates

REDWHITE

BLUE

Chevy Ford

By Make

By Color

Sum

Cross TabRED

WHITE

BLUE

By Color

Sum

Group By (with total)Sum

Aggregate

Data Analysis Tasks

Formulate a query to extract relevant information

Extract the aggregated data from the database into a file.

Visualize the result to look for patterns. Analyze the result and perhaps formulate

new queries.

Goal of the Cube

Represent N-dimensional data cubes using 2-D relations

Compute the aggregate information within the database,

Create language extensions to SQL that will support the required operations.

Relational Representation of N-Dimensional Data

Weather information consists of four dimensions: Time, Latitude, Longitude and Altitude

Measured variables are Temperature and Pressure.

SQL 1992 Standard supports aggregation functions:

•Count (), Sum(), Min(), Max() and AVE(); Some SQL

systems have Median, Standard Deviation, variance, etc.

•SELECT AVE(Temp) FROM Weather

•SELECT COUNT(DISTINCT Time) FROM Weather

SELECT Time, Altitude, AVE(Temp)

FROM Weather

GROUP BY Time, Altitude

Group-By Operation and Aggregates

Group-By operation defines an equivalence relation on a table, partitioning the tuples in classes having the same attribute values.

Each group can then be aggregated.

TCP-D has one 6D GROUP BY and three 3D GROUP Bys.

Problems with GROUP-BY

Group-By cannot directly construct: Histograms, Roll-up Totals, Sub-totals for Drill-downs, and Cross Tabulations.

Roll-Ups and Sub-Totals for Drill-Down

Tables 3a, 3b, and 4 show Sales Roll Up by Model by Year by Color and an Excel Pivot Table with this information

Roll-Ups and Sub-Totals for Drill-Down

Excel Pivot Table

Sales Summary Roll Up in Table 5a and 5b for Chevy over all color and years (Note the ALL value)

SQL Query for Chevy Roll Up

Cube: Each Attribute is a DimensionCube: Each Attribute is a Dimension

N-dimensional Aggregate (sum(), max(),...) Fits relational model exactly:

a1, a2, ...., aN, f()

Super-aggregate over N-1 Dimensional sub-cubes ALL, a2, ...., aN , f() a1 , ALL, a3, ...., aN , f() ... a1, a2, ...., ALL, f()

This is the N-1 Dimensional cross-tab. Super-aggregate over N-2 Dimensional sub-cubes

ALL, ALL, a3, ...., aN , f() ... a1, a2 ,...., ALL, ALL, f()

Sub-cube Derivation

Dimension collapse, * denotes ALL

<M,Y,C>

<M,Y,*> <M,*,C> <*,Y,C>

<M,*,*> <*,Y,*><*,*,C>

<*,*,*>

Cube Operator ExampleCube Operator Example SALES Model Year Color Sales Chevy 1990 red 5 Chevy 1990 white 87 Chevy 1990 blue 62 Chevy 1991 red 54 Chevy 1991 white 95 Chevy 1991 blue 49 Chevy 1992 red 31 Chevy 1992 white 54 Chevy 1992 blue 71 Ford 1990 red 64 Ford 1990 white 62 Ford 1990 blue 63 Ford 1991 red 52 Ford 1991 white 9 Ford 1991 blue 55 Ford 1992 red 27 Ford 1992 white 62 Ford 1992 blue 39

DATA CUBE Model Year Color Sales ALL ALL ALL 942 chevy ALL ALL 510 ford ALL ALL 432 ALL 1990 ALL 343 ALL 1991 ALL 314 ALL 1992 ALL 285 ALL ALL red 165 ALL ALL white 273 ALL ALL blue 339 chevy 1990 ALL 154 chevy 1991 ALL 199 chevy 1992 ALL 157 ford 1990 ALL 189 ford 1991 ALL 116 ford 1992 ALL 128 chevy ALL red 91 chevy ALL white 236 chevy ALL blue 183 ford ALL red 144 ford ALL white 133 ford ALL blue 156 ALL 1990 red 69 ALL 1990 white 149 ALL 1990 blue 125 ALL 1991 red 107 ALL 1991 white 104 ALL 1991 blue 104 ALL 1992 red 59 ALL 1992 white 116 ALL 1992 blue 110

CUBE

Interesting Aggregate Functions From RedBrick systems

Rank (in sorted order) N-Tile (histograms) Running average (cumulative functions) Windowed running average Percent of total

Users want to define their own aggregate functions statistics domain specific


Techniques 27

Conceptual Modeling of Data Warehouses

Modeling data warehouses: dimensions & measures

Star schema: A fact table in the middle connected to a

set 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 constellation


Techniques 28

Example of Star Schema

time_keydayday_of_the_weekmonthquarteryear

time

location_keystreetcitystate_or_provincecountry

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures

item_keyitem_namebrandtypesupplier_type

item

branch_keybranch_namebranch_type

branch


Techniques 29

Example of Snowflake Schema


time

location_keystreetcity_key

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures

item_keyitem_namebrandtypesupplier_key

item


branch

supplier_keysupplier_type

supplier

city_keycitystate_or_provincecountry

city


Techniques 30

Example of Fact Constellation


time

location_keystreetcityprovince_or_statecountry

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures

item_keyitem_namebrandtypesupplier_type

item


branch

Shipping Fact Table

time_key

item_key

shipper_key

from_location

to_location

dollars_cost

units_shipped

shipper_keyshipper_namelocation_keyshipper_type

shipper


Techniques 31

A Concept Hierarchy: Dimension (location)

all

Europe North_America

MexicoCanadaSpainGermany

Vancouver

M. WindL. Chan

...

......

... ...

...

all

region

office

country

TorontoFrankfurtcity


Techniques 32

Data Cube Measures: Three Categories

Distributive: if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partitioning

E.g., count(), sum(), min(), max()

Algebraic: if it can be computed by an algebraic function with M arguments (where M is a bounded integer), each of which is obtained by applying a distributive aggregate function

E.g., avg(), min_N(), standard_deviation()

Holistic: if there is no constant bound on the storage size needed to describe a subaggregate.

E.g., median(), mode(), rank()


Techniques 33

Typical OLAP Operations over Data Cube

Roll up (drill-up): summarize data by climbing up hierarchy or by dimension reduction

Drill down (roll down): reverse of roll-up from higher level summary to lower level summary or

detailed data, or introducing new dimensions Slice and dice: project and select Pivot (rotate):

reorient the cube, visualization, 3D to series of 2D planes Other operations

drill across: involving (across) more than one fact table drill through: through the bottom level of the cube to its

back-end relational tables (using SQL)


Techniques 34

Definition of Cube Operation A new operator cube by, introduced by Gray et al.’96

SELECT item, city, year, SUM (amount)

FROM SALES

CUBE BY item, city, year

Need compute the following Group-Bys

(date, product, customer),

(date,product),(date, customer), (product, customer),

(date), (product), (customer)

()

(item)(city)

()

(year)

(city, item) (city, year) (item, year)

(city, item, year)


Techniques 35

Cube: A Lattice of Cuboids

time,item

time,item,location

time, item, location, supplier

all

time item location supplier

time,location

time,supplier

item,location

item,supplier

location,supplier

time,item,supplier

time,location,supplier

item,location,supplier

0-D (apex) cuboid

1-D cuboids

2-D cuboids

3-D cuboids

4-D (base) cuboid


Techniques 36

Iceberg Cube

Computing only the cuboid cells whose count or other aggregates satisfy a condition with minimum support, e.g.

HAVING COUNT(*) >= minsup


Techniques 37

Indexing OLAP Data: Bitmap Index Index on a particular column

Each value in the column has a bit vector: bit-op is fast The length of the bit vector: # of records in the base table The i-th bit is set if the i-th row of the base table has the value for the

indexed column A recent bit compression technique, Word-Aligned Hybrid (WAH),

makes it work for high cardinality domain as well [Wu, et al. TODS’06]

Cust Region TypeC1 Asia RetailC2 Europe DealerC3 Asia DealerC4 America RetailC5 Europe Dealer

RecID Retail Dealer1 1 02 0 13 0 14 1 05 0 1

RecID Asia Europe America1 1 0 02 0 1 03 1 0 04 0 0 15 0 1 0

Base table Index on Region Index on Type


Techniques 38

Indexing OLAP Data: Join Indices Join index:

JI(R-id, S-id) where R (R-id, …) S (S-id, …)

In data warehouses, join index relates the values of the dimensions of a star schema to rows in the fact table. E.g. fact table: Sales and two dimensions

city and product A join index on city maintains for each

distinct city a list of R-IDs of the tuples recording the Sales in the city

Join indices can span multiple dimensions


Techniques 39

Efficient Processing OLAP Queries

Determine which operations should be performed on the available cuboids

Transform drill, roll, etc. into corresponding SQL and/or OLAP operations,

e.g., dice = selection + projection

Determine which materialized cuboid(s) should be selected for OLAP op.

Let the query to be processed be on {brand, province_or_state} with the

condition “year = 2004”, and there are 4 materialized cuboids available:

1) {year, item_name, city}

2) {year, brand, country}

3) {year, brand, province_or_state}

4) {item_name, province_or_state} where year = 2004

Which should be selected to process the query?

Explore indexing structures and compressed vs. dense array structs in MOLAP


Techniques 40

OLAP Server Architectures Relational OLAP (ROLAP)

Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware

Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services

Greater scalability Multidimensional OLAP (MOLAP)

Sparse array-based multidimensional storage engine Fast indexing to pre-computed summarized data

Hybrid OLAP (HOLAP) (e.g., Microsoft SQLServer) Flexibility, e.g., low level: relational, high-level: array

Summary

Decision support is an emerging, rapidly growing subarea of databases.

Involves the creation of large, consolidated data repositories called data warehouses.

Warehouses exploited using sophisticated analysis techniques: complex SQL queries and OLAP “multidimensional”queries (influenced by both SQL and spreadsheets).

New techniques for database design, indexing, view maintenance, and interactive querying need to be supported.

Documents

Data Warehousing and Decision Support courtesy of Jiawei Han, Larry Kerschberg, and etc. for some slides. Jianlin Feng School of Software SUN YAT-SEN UNIVERSITY