WebBase%20and%20Stanford%20Digital%20Library%20Project

1
WebBase and Stanford Digital Library Project

• Junghoo Cho
• Stanford University

2
Technologies for Digital Libraries
Physical Barriers

• Mobile Access

Economic Weaknesses

• IP Infrastructure

Information Loss

• Archival Repository

Information Overload

• Value Filtering

Service Heterogeneity

• Interoperability

2
3
WebBase Architecture
Client
Client
Webbase API
WWW
Retrieval Indexes
Feature Repository
Repository
Multicast Engine
Client
Client
Client
Client
3
4
What is a Crawler?
initial urls
init
to visit urls
get next url
web
get page
visited urls
extract urls
web pages
5
Crawling Issues (1)

• Load at visited web sites
• Load at crawlers
• Scope of the crawl

6
Crawling Issues (2)

• Maintaining pages fresh
• How does the web change over time?
• What does fresh page/database mean?
• How can we increase freshness?

7
Outline

• Web evolution experiments
• Freshness metrics
• Crawling policy

8
Web Evolution Experiment

• How often does a web page change?
• What is the lifespan of a page?
• How long does it take for 50 of the web to
  change?

9
Experimental Setup

• February 17 to June 24, 1999
• 270 sites visited (with permission)
• identified 400 sites with highest page rank
• contacted administrators
• 720,000 pages collected
• 3,000 pages from each site daily
• start at root, visit breadth first (get new old
  pages)
• ran only 9pm - 6am, 10 seconds between site
  requests

10
Page Change

• Example 50 visits to page, 5 changes
• ? average change interval 50/5 10 days

11
Average Change Interval
fraction of pages
12
Change Interval - By Domain
fraction of pages
13
Modeling Web Evolution

• Poisson process with rate ?
• T is time to next event
• fT(t) ?e-?t (t gt 0)

14
Change Interval of Pages
for pages that change every 10 days on average
fraction of changes with given interval
Poisson model
interval in days
15
Change Metrics

• Freshness
• Freshness of page ei at time t is F( ei
  t ) 1 if ei is up-to-date at time t
  0 otherwise

16
Change Metrics

• Age
• Age of page ei at time t is A( ei t )
  0 if ei is up-to-date at time t
  t - (modification ei time)
  otherwise

17
Change Metrics
F(ei)
1
0
time
A(ei)
0
time
refresh
update
18
Trick Question

• Two page database
• e1 changes daily
• e2 changes once a week
• Can visit pages once a week
• How should we visit pages?
• e1 e1 e1 e1 e1 e1 ...
• e2 e2 e2 e2 e2 e2 ...
• e1 e2 e1 e2 e1 e2 ... uniform
• e1 e1 e1 e1 e1 e1 e1 e2 e1 e1 ...
  proportional
• ?

e1
e1
e2
e2
web
database
19
Proportional Often Not Good!

• Visit fast changing e1 ? get 1/2 day of freshness
• Visit slow changing e2 ? get 1/2 week of
  freshness
• Visiting e2 is a better deal!

20
Selecting Optimal Refresh Frequency

• Analysis is complex
• Shape of curve is the same in all cases
• Holds for any distribution g( ? )

21
Optimal Refresh Frequency for Age

• Analysis is also complex
• Shape of curve is the same in all cases
• Holds for any distribution g( ? )

22
Comparing Policies
Based on Statistics from experiment and revisit
frequency of every month
23
Summary

• Maintaining the collection fresh
• Web evolution experiment
• Change metrics
• Optimal policy
• Intuitive policy does not always perform well
• Should be careful in deciding revisit policy