Data Warehousing Overview CS245 Notes 11 Hector Garcia-Molina Stanford University CS 245 1 Notes11

Data Warehousing OverviewCS245 Notes 11

Hector Garcia-Molina

Stanford University

CS 2451

Notes11

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Outline

What is a data warehouse? Why a warehouse? Models & operations Implementing a warehouse

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What is a Warehouse?

Collection of diverse data subject oriented aimed at executive, decision maker often a copy of operational data with value-added data (e.g., summaries, history)

integrated time-varying non-volatile

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What is a Warehouse?

Collection of tools gathering data cleansing, integrating, ... querying, reporting, analysis data mining monitoring, administering warehouse

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Warehouse Architecture

Client Client

Warehouse

Source Source Source

Query & Analysis

Integration

Metadata

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Motivating Examples

Forecasting Comparing performance of units Monitoring, detecting fraud Visualization

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Alternative to Warehousing

Two Approaches: Query-Driven (Lazy) Warehouse (Eager)

Source Source

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Query-Driven Approach

Client Client

Wrapper Wrapper Wrapper

Mediator

Source Source Source

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Advantages of Warehousing

High query performance Queries not visible outside warehouse Local processing at sources unaffected Can operate when sources unavailable Can query data not stored in a DBMS Extra information at warehouse

Modify, summarize (store aggregates) Add historical information

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Advantages of Query-Driven

No need to copy data less storage no need to purchase data

More up-to-date data Query needs can be unknown Only query interface needed at sources May be less draining on sources

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Warehouse Models & Operators

Data Models relational cubes

Operators

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customer custId name address city53 joe 10 main sfo81 fred 12 main sfo

111 sally 80 willow la

product prodId name pricep1 bolt 10p2 nut 5

store storeId cityc1 nycc2 sfoc3 la

sale oderId date custId prodId storeId qty amto100 1/7/97 53 p1 c1 1 12o102 2/7/97 53 p2 c1 2 11105 3/8/97 111 p1 c3 5 50

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Star Schema

saleorderId

datecustIdprodIdstoreId

qtyamt

customercustIdname

addresscity

productprodIdnameprice

storestoreId

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Fact table Dimension tables Measures

saleorderId

datecustIdprodIdstoreId

qtyamt

customercustIdname

addresscity

productprodIdnameprice

storestoreId

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Dimension Hierarchies

store storeId cityId tId mgrs5 sfo t1 joes7 sfo t2 freds9 la t1 nancy

city cityId pop regIdsfo 1M northla 5M south

region regId namenorth cold regionsouth warm region

sType tId size locationt1 small downtownt2 large suburbs

storesType

city region

snowflake schema constellations

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sale prodId storeId amtp1 c1 12p2 c1 11p1 c3 50p2 c2 8

c1 c2 c3p1 12 50p2 11 8

Fact table view: Multi-dimensional cube:

dimensions = 2

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3-D Cube

sale prodId storeId date amtp1 c1 1 12p2 c1 1 11p1 c3 1 50p2 c2 1 8p1 c1 2 44p1 c2 2 4

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

dimensions = 3

Multi-dimensional cube:Fact table view:

Operators

Traditional selection aggregation ...

Analysis clean data find trends ...

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Relational

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Aggregates

• Add up amounts for day 1• In SQL: SELECT sum(amt) FROM SALE WHERE date = 1

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Aggregates

• Add up amounts by day• In SQL: SELECT date, sum(amt) FROM SALE GROUP BY date

ans date sum1 812 48

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Another Example

• Add up amounts by day, product• In SQL: SELECT date, sum(amt) FROM SALE GROUP BY date, prodId

sale prodId date amtp1 1 62p2 1 19p1 2 48

drill-down

rollup

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Aggregates

Operators: sum, count, max, min, median, ave

“Having” clause Using dimension hierarchy

average by region (within store) maximum by month (within date)

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Cube Aggregation

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

c1 c2 c3p1 56 4 50p2 11 8

c1 c2 c3sum 67 12 50

sump1 110p2 19

drill-down

rollup

Example: computing sums

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Cube Operators

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

c1 c2 c3p1 56 4 50p2 11 8

c1 c2 c3sum 67 12 50

sump1 110p2 19

sale(c1,*,*)

sale(*,*,*)sale(c2,p2,*)

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c1 c2 c3 *p1 56 4 50 110p2 11 8 19* 67 12 50 129

Extended Cube

day 2 c1 c2 c3 *p1 44 4 48p2* 44 4 48

c1 c2 c3 *p1 12 50 62p2 11 8 19* 23 8 50 81

sale(*,p2,*)

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Aggregation Using Hierarchies

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

region A region Bp1 56 54p2 11 8

customer

region

country

(customer c1 in Region A;customers c2, c3 in Region B)

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Data Analysis

Decision Trees Clustering Association Rules

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Decision Trees

sale custId car age city newCarc1 taurus 27 sf yesc2 van 35 la yesc3 van 40 sf yesc4 taurus 22 sf yesc5 merc 50 la noc6 taurus 25 la no

Example:• Conducted survey to see what customers were interested in new model car• Want to select customers for advertising campaign

trainingset

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One Possibility

age<30

city=sf car=van

likely likelyunlikely unlikely

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Another Possibility

car=taurus

city=sf age<45

likely likelyunlikely unlikely

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Issues

Decision tree cannot be “too deep” would not have statistically significant amounts of

data for lower decisions

Need to select tree that most reliably predicts outcomes

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Clustering

income

education

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Clustering

income

education

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Another Example: Text

Each document is a vector e.g., <100110...> contains words 1,4,5,...

Clusters contain “similar” documents Useful for understanding, searching

documents

internationalnews

sports

business

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Issues

Given desired number of clusters? Finding “best” clusters Are clusters semantically meaningful?

e.g., “yuppies’’ cluster? Using clusters for disk storage

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Association Rule Mining

tran1 cust33 p2, p5, p8tran2 cust45 p5, p8, p11tran3 cust12 p1, p9tran4 cust40 p5, p8, p11tran5 cust12 p2, p9tran6 cust12 p9

transactio

id custo

id products

bought

salesrecords:

• Trend: Products p5, p8 often bough together• Trend: Customer 12 likes product p9

market-basketdata

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Association Rule

Rule: {p1, p3, p8} Support: number of baskets where these

products appear High-support set: support threshold s Problem: find all high support sets

Implementation Issues

ETL (Extraction, transformation, loading) Getting data to the warehouse Entity Resolution

What to materialize? Efficient Analysis

Association rule mining ...

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ETL: Monitoring Techniques

Periodic snapshots Database triggers Log shipping Data shipping (replication service) Transaction shipping Polling (queries to source) Screen scraping Application level monitoring

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ETL: Data Cleaning

Migration (e.g., yen dollars) Scrubbing: use domain-specific knowledge (e.g.,

social security numbers) Fusion (e.g., mail list, customer merging)

Auditing: discover rules & relationships(like data mining)

billing DB

service DB

customer1(Joe)

customer2(Joe)

merged_customer(Joe)

More details: Entity Resolution

N: a A: b CC#: c Ph: e

N: a Exp: d Ph: e

Applications

comparison shopping mailing lists classified ads customer files counter-terrorism

N: a A: b CC#: c Ph: e

N: a Exp: d Ph: e

Why is ER Challenging?

Huge data sets No unique identifiers Lots of uncertainty Many ways to skin the cat

Taxonomy: Pairwise vs Global

Decide if r, s match only by looking at r, s? Or need to consider more (all) records?

Nm: Pat SmithAd: 123 Main StPh: (650) 555-1212

Nm: Patrick SmithAd: 132 Main StPh: (650) 555-1212

Nm: Patricia SmithAd: 123 Main StPh: (650) 777-1111

Taxonomy: Pairwise vs Global

Global matching complicates things a lot! e.g., change decision as new records arrive

Nm: Patrick SmithAd: 132 Main StPh: (650) 555-1212

Nm: Patricia SmithAd: 123 Main StPh: (650) 777-1111

Taxonomy: Outcome

Partition of records e.g., comparison shopping

Merged records

Nm: Patricia SmithAd: 123 Main StPh: (650) 555-1212 (650) 777-1111Hair: Black

Nm: Patricia SmithAd: 132 Main StPh: (650) 777-1111Hair: Black

Taxonomy: Outcome Iterate after merging

Nm: TomWk: IBMOc: laywerSal: 500K

Nm: TomAd: 123 MainBD: Jan 1, 85Wk: IBM

Nm: ThomasAd: 123 MaimOc: lawyer

Nm: TomAd: 123 MainBD: Jan 1, 85Wk: IBMOc: lawyer

Nm: TomAd: 123 MainBD: Jan 1, 85Wk: IBMOc: lawyerSal: 500K

Taxonomy: Record Reuse One record related to multiple entities?

Nm: Pat Smith Sr.Ph: (650) 555-1212

Ph: (650) 555-1212Ad: 123 Main St

Nm: Pat Smith Jr.Ph: (650) 555-1212

Nm: Pat Smith Sr.Ph: (650) 555-1212Ad: 123 Main St

Nm: Pat Smith Jr.Ph: (650) 555-1212Ad: 123 Main St

Taxonomy: Record Reuse

Partitions

• Merges

r s t r

Taxonomy: Record Reuse

Partitions

• Merges

r s t r

• Record reuse complex and expensive!

Taxonomy: Multiple Entity Types

person 1 person 2

Organization B

Organization Abrother

member

business

member

Taxonomy: Multiple Entity Types

authors papers

same??

Taxonomy: Exact vs Approximate

products

camerasresolvedcameras

resolvedCDs

resolvedbooks

Taxonomy: Exact vs Approximate

terrorists terroristssortby age

B Cooper 30match against

ages 25-35

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What to Materialize?

Store in warehouse results useful for common queries

Example:day 2

c1 c2 c3p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

c1 c2 c3p1 56 4 50p2 11 8

c1 c2 c3p1 67 12 50

c1p1 110p2 19

total sales

materialize

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Materialization Factors

Type/frequency of queries Query response time Storage cost Update cost

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Cube Aggregates Lattice

city, product, date

city, product city, date product, date

city product date

day 2c1 c2 c3

p1 44 4p2 c1 c2 c3

p1 12 50p2 11 8

c1 c2 c3p1 56 4 50p2 11 8

c1 c2 c3p1 67 12 50

use greedyalgorithm todecide whatto materialize

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cities city statec1 CAc2 NY

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city, product

city, product, date

city, date product, date

city product date

state, product, date

state, date

state, product

not all arcs shown...

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Interesting Hierarchy

quarters

months

time day week month quarter year1 1 1 1 20002 1 1 1 20003 1 1 1 20004 1 1 1 20005 1 1 1 20006 1 1 1 20007 1 1 1 20008 2 1 1 2000

conceptualdimension table

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Finding High-Support Pairs

Baskets(basket, item) SELECT I.item, J.item, COUNT(I.basket)

FROM Baskets I, Baskets JWHERE I.basket = J.basket AND I.item < J.itemGROUP BY I.item, J.itemHAVING COUNT(I.basket) >= s;

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Finding High-Support Pairs

Baskets(basket, item) SELECT I.item, J.item, COUNT(I.basket)

FROM Baskets I, Baskets JWHERE I.basket = J.basket AND I.item < J.itemGROUP BY I.item, J.itemHAVING COUNT(I.basket) >= s;

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Example

basket itemt1 p2t1 p5t1 p8t2 p5t2 p8t2 p11... ...

basket item1 item2t1 p2 p5t1 p2 p8t1 p5 p8t2 p5 p8t2 p5 p11t2 p8 p11... ... ...

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Example

basket itemt1 p2t1 p5t1 p8t2 p5t2 p8t2 p11... ...

check ifcount s

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Issues

Performance for size 2 rulesbasket item

t1 p2t1 p5t1 p8t2 p5t2 p8t2 p11... ...

bigevenbigger!

Performance for size k rules

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Association Rules

How do we perform rule mining efficiently?

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Association Rules

How do we perform rule mining efficiently? Observation: If set X has support t, then

each X subset must have at least support t

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Association Rules

How do we perform rule mining efficiently? Observation: If set X has support t, then

each X subset must have at least support t For 2-sets:

if we need support s for {i, j} then each i, j must appear in at least s

baskets

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Algorithm for 2-Sets

(1) Find OK products those appearing in s or more baskets

(2) Find high-support pairs using only OK products

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INSERT INTO okBaskets(basket, item) SELECT basket, item FROM Baskets GROUP BY item HAVING COUNT(basket) >= s;

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INSERT INTO okBaskets(basket, item) SELECT basket, item FROM Baskets GROUP BY item HAVING COUNT(basket) >= s;

Perform mining on okBaskets SELECT I.item, J.item, COUNT(I.basket) FROM okBaskets I, okBaskets J WHERE I.basket = J.basket AND I.item < J.item GROUP BY I.item, J.item HAVING COUNT(I.basket) >= s;

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Counting Efficiently

One way:

basket I.item J.itemt1 p5 p8t2 p5 p8t2 p8 p11t3 p2 p3t3 p5 p8t3 p2 p8... ... ...

threshold = 3

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One way:

threshold = 3

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One way:

count &remove count I.item J.item

3 p5 p85 p12 p18... ... ...

threshold = 3

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Another way:

threshold = 3

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Another way:

scan &count

count I.item J.item1 p2 p32 p2 p83 p5 p85 p12 p181 p21 p222 p21 p23... ... ...

keep counterarray in memory

threshold = 3

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Another way:

remove count I.item J.item3 p5 p85 p12 p18... ... ...

scan &count

threshold = 3

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Another way:

remove count I.item J.item3 p5 p85 p12 p18... ... ...

scan &count

threshold = 3

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Yet Another Way

(1)scan &hash &count

count bucket1 A5 B2 C1 D8 E1 F... ...

in-memoryhash table threshold = 3

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Yet Another Way

(2) scan &remove

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Yet Another Way

(2) scan &remove

false positive

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Yet Another Way

(2) scan &remove

count I.item J.item3 p5 p81 p8 p115 p12 p18... ... ...

(3) scan& count

in-memorycounters

false positive

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Yet Another Way

(2) scan &remove

count I.item J.item3 p5 p85 p12 p18... ... ...

(4) removecount I.item J.item

3 p5 p81 p8 p115 p12 p18... ... ...

(3) scan& count

in-memorycounters

false positive

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Discussion

Hashing scheme: 2 (or 3) scans of data Sorting scheme: requires a sort! Hashing works well if few high-support pairs

and many low-support ones

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Discussion

Hashing scheme: 2 (or 3) scans of data Sorting scheme: requires a sort! Hashing works well if few high-support pairs

and many low-support ones

item-pairs ranked by frequency

threshold

iceberg queries

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Extra: Data Mining in the InfoLab

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quarters

Recommendations in CourseRank

user q1 q2 q3 q4u1 a: 5 b: 5 d: 5 u2 a: 1 e: 2 d: 4 f: 3u3 g: 4 h: 2 e: 3 f: 3u4 b: 2 g: 4 h: 4 e: 4u a: 5 g: 4 e: 4

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quarters

u3 and u4 are similar to u Recommend h

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quarters

Recommend d (and f, h)

Sequence Mining

Given a set of transcripts, use Pr[x|a]to predict if x is a good recommendationgiven user has taken a.

Two issues...

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Pr[x|a] Not Quite Right

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transcript containing1 -2 a3 x4 a -> x5 x -> a

Pr[x|a] = 2/3

Pr[x|a~x] = 1/2

target user’s transcript:[ ... a .... || unknown ]

recommend x?

User Has Taken >= 1 Course

User has taken T= {a, b, c} Need Pr[x|T~x] Approximate as Pr[x|a~x b~x c~x ] Expensive to compute, so...

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CourseRank User Study

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good, expected

good, unexpected

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