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Searching the WebSearching the Web

Junghoo ChoJunghoo Cho

UCLA Computer ScienceUCLA Computer Science

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Legacy database Plain text files

Biblio sever

Information GaloreInformation Galore

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Information Overload ProblemInformation Overload Problem

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SolutionSolution

Indexing approachIndexing approach– Google, Excite, AltaVistaGoogle, Excite, AltaVista

Integration approachIntegration approach– MySimon, BizRateMySimon, BizRate

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Indexing ApproachIndexing Approach

Central Index

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ChallengesChallenges

Page selection and downloadPage selection and download– What page to download?What page to download?

Page and index updatePage and index update– How to update pages?How to update pages?

Page rankingPage ranking– What page is “important” or “relevant”?What page is “important” or “relevant”?

ScalabilityScalability

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Integration ApproachIntegration Approach

Mediator

Wrapper

Source 1

Wrapper

Source 2

Wrapper

Source n

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Heterogeneous sourcesHeterogeneous sources– Different data models: relational, object-orientedDifferent data models: relational, object-oriented– Different schemas and representations:Different schemas and representations:

““Keanu ReevesKeanu Reeves” or “” or “Reeves, K.Reeves, K.” etc.” etc. Limited query capabilitiesLimited query capabilities Mediator cachingMediator caching

ChallengesChallenges

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Focus of the TalkFocus of the Talk

Indexing approachIndexing approach How to maintain pages up-to-date?How to maintain pages up-to-date?

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Outline of This TalkOutline of This Talk

How can we maintain pages fresh?How can we maintain pages fresh? How does the Web change?How does the Web change? What do we mean by “fresh” pages?What do we mean by “fresh” pages? How should we refresh pages?How should we refresh pages?

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Web Evolution ExperimentWeb Evolution Experiment

How often does a Web page change?How often does a Web page change? How long does a page stay on the Web?How long does a page stay on the Web? How long does it take for 50% of the Web How long does it take for 50% of the Web

to change?to change? How do we model Web changes?How do we model Web changes?

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Experimental SetupExperimental Setup

February 17 to June 24, 1999February 17 to June 24, 1999 270 sites visited (with permission)270 sites visited (with permission)

– identified 400 sites with highest “PageRank”identified 400 sites with highest “PageRank”– contacted administratorscontacted administrators

720,000 pages collected720,000 pages collected– 3,000 pages from each site daily3,000 pages from each site daily– start at root, visit breadth first (get new & old pages)start at root, visit breadth first (get new & old pages)– ran only 9pm - 6am, 10 seconds between site requestsran only 9pm - 6am, 10 seconds between site requests

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Average Change IntervalAverage Change Intervalfr

actio

n of

pag

es

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

1day 1day- 1week

1week-1month

1month-4months

4months

average change interval

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Change Interval – By DomainChange Interval – By Domainfr

actio

n of

pag

es

0

0.1

0.2

0.3

0.4

0.5

0.6

1day 1day- 1week

1week-1month

1month-4months

4months

com

netorg

edu

gov

average change interval

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Modeling Web EvolutionModeling Web Evolution

Poisson process with rate Poisson process with rate T is time to next eventT is time to next event ffTT ((tt) = ) = ee--

tt ( (tt > 0) > 0)

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Change Interval of PagesChange Interval of Pagesfor pages thatchange every

10 days on average

interval in days

frac

tion

of c

hang

esw

ith g

iven

inte

rval

Poisson model

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Change MetricsChange Metrics

FreshnessFreshness– Freshness of element Freshness of element eeii at time at time tt is is

F F ( ( eeii ; ; tt ) = 1 if ) = 1 if eeii is up-to-date at time is up-to-date at time tt 0 otherwise 0 otherwise

eiei

......

web databaseFreshness of the database S at time t is

F( S ; t ) = F( ei ; t )

(Assume “equal importance” of pages)

N

1 N

i=1

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Change MetricsChange Metrics

AgeAge– Age of element Age of element eeii at time at time tt is is

A A( ( eeii ; ; tt ) = 0 if ) = 0 if eeii is up-to-date at time is up-to-date at time tt tt - (modification - (modification eei i time) otherwisetime) otherwise

eiei

......

web databaseAge of the database S at time t is

A( S ; t ) = A( ei ; t )

(Assume “equal importance” of pages)

N

1 N

i=1

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Change MetricsChange Metrics

F(ei)

A(ei)

0

0

1

time

time

update refresh

Time averages:

0

1( ) lim ( ; )

t

i itF e F e t dt

t

0

1( ) lim ( ; )

t

tF S F S t dt

t

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Trick QuestionTrick Question

Two page databaseTwo page database e1 changes dailychanges daily e2 changes once a weekchanges once a week Can visit one page per weekCan visit one page per week How should we visit pages?How should we visit pages?

– e1 e2 e1 e2 e1 e2 e1 e2... ... [uniform] [uniform]

– e1 e1 e1 e1 e1 e1 e1 e2 e1 e1 … … [proportional][proportional]

– e1 e1 e1 e1 e1 e1 ... ...

– e2 e2 e2 e2 e2 e2 ... ...

– ??

e1

e2

e1

e2

webdatabase

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Proportional Often Not Good!Proportional Often Not Good!

Visit fast changing Visit fast changing e1

get 1/2 day of freshnessget 1/2 day of freshness

Visit slow changing Visit slow changing e2

get 1/2 week of freshnessget 1/2 week of freshness

Visiting Visiting e2 is a better deal!is a better deal!

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Optimal Refresh FrequencyOptimal Refresh Frequency

ProblemProblem

Given and Given and f ,f ,

findfind

that maximizethat maximize

1 2, , ..., N

1 21

, ,... , /N

N

ii

f f f f f N

1

1( ) ( )

N

ii

F S F eN

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Optimal Refresh FrequencyOptimal Refresh Frequency

• Shape of curve is the same in all cases• Holds for any change frequency distribution

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Optimal Refresh for AgeOptimal Refresh for Age

• Shape of curve is the same in all cases• Holds for any change frequency distribution

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Comparing PoliciesComparing Policies

Freshness AgeProportional 0.12 400 days

Uniform 0.57 5.6 daysOptimal 0.62 4.3 days

Based on Statistics from experimentand revisit frequency of every month

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Not Every Page is Equal!Not Every Page is Equal!

1 211

( ) ( ) (2 )3

F S F e F e

In general,1 1

( ) ( )N N

i i ii i

F S w F e w

e1

e2 Accessed by users 20 times/day

Accessed by users 10 times/day

Some pages are “more important”Some pages are “more important”

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Weighted FreshnessWeighted Freshness

w = 1

w = 2

f

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Change Frequency EstimationChange Frequency Estimation

How to estimate change frequency?How to estimate change frequency?– Naïve Estimator: Naïve Estimator: XX//TT

– XX: number of detected changes: number of detected changes

– TT: monitoring period: monitoring period

– 2 changes in 10 days: 0.2 times/day2 changes in 10 days: 0.2 times/day

Change detected1 day

Page visitedPage changed

Incomplete change historyIncomplete change history

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Improved EstimatorImproved Estimator

Based on the Poisson modelBased on the Poisson model

– XX: number of detected changes: number of detected changes– NN: number of accesses: number of accesses– f f : access frequency: access frequency

2log

1

Nf

N X

3 changes in 10 days: 0.36 times/day Accounts for “missed” changes

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Improvement Significant?Improvement Significant?

Application to a Web crawlerApplication to a Web crawler– Visit pages once every week for 5 weeksVisit pages once every week for 5 weeks– Estimate change frequency Estimate change frequency – Adjust revisit frequency based on the estimateAdjust revisit frequency based on the estimate

» Uniform: do not adjustUniform: do not adjust

» Naïve: based on the naïve estimatorNaïve: based on the naïve estimator

» Ours: based on our improved estimatorOurs: based on our improved estimator

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Improvement from Our EstimatorImprovement from Our Estimator

Detected changesDetected changes Ratio to uniformRatio to uniform

UniformUniform 2,147,5892,147,589 100%100%

NaïveNaïve 4,145,5824,145,582 193%193%

OursOurs 4,892,1164,892,116 228%228%

(9,200,000 visits in total)

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SummarySummary

Information overload problemInformation overload problem– Indexing approachIndexing approach– Integration approachIntegration approach

Page updatePage update– Web evolution experimentWeb evolution experiment– Change metricChange metric– Refresh policyRefresh policy– Frequency estimatorFrequency estimator

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Research OpportunityResearch Opportunity

Efficient query processing?Efficient query processing? Automatic source discovery?Automatic source discovery? Automatic data extraction?Automatic data extraction?

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Web Archive ProjectWeb Archive Project

Can we store the history of the Web?Can we store the history of the Web?– Web is ephemeralWeb is ephemeral– Study of the Evolution of the WebStudy of the Evolution of the Web

ChallengesChallenges– Update policy?Update policy?– Compression?Compression?– New storage structure?New storage structure?– New index structure?New index structure?

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The EndThe End

Thank you for your attentionThank you for your attention For more information visitFor more information visit

http://www.cs.ucla.edu/~cho/http://www.cs.ucla.edu/~cho/