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Data Miningat work
Krithi Ramamritham
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Dynamics of Web Data Dynamically created Web Pages
-- using scripting languages
Ad Component
Headline Component
Headline Component
Headline Component
Headline Component
Personalized Component
Navig
ati
on C
om
ponent
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1. What to deliver?
Page content may be based on • queries on dynamically changing data
– e.g., sports scores, stock prices, environment
• type of access device• time and location of access/user
Existing sites may contain new information
New sites (URLs) may come into being
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2. How to deliver?
Data sources
Proxies/caches
End-hosts
servers
sensors
wired host
mobile host
Netw
ork
Netw
ork
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Keep Data Up-to-date
• Update Mumbai temperature every 2 degrees
• The proxy obtains data from the source(s)
• Maintains | | UU((tt) - ) - SS((tt) | <= ) | <= 22
SourceS(t)
Proxy / DBP(t)
UserU(t)
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When to poll the source?
After a specific interval
Server Proxy UserPull
Based on temporal data mining – time series analysis – and prediction of when change will exceed 2 degrees
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Where to do the work?
• Diverse client devices– Differ in hardware, software,
network connectivity,
form factor
• Web content needs to be tailored for each client type
Each response depends not only on the requested URL but also on the capabilities of the client
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Transcoding
Conversion of one data version to another–Decreasing Image Quality (JPEG quality level) and size
- “convert” utility in Linux–Summarizing text
transcode =>
Info extraction/retrieval/
classification
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Who should transcode?
1. Download desired version from server
2. Transcode higher version locally
• Factors influencing decision– Transcoding Complexity– Proxy-server network connection – Load on proxy
(Multiple Linear) Regression Predict based on a (linear) model of overheads
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What is new on the Web?
How is the monsoon progressing?
Time series analysis:Change prediction, pattern mining
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‘Bhav Puchiye’
www.broadmoor.com
Interface for Bhav Puchiye
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Inverted Pyramid Interfaces
Inverted pyramid approach
Conclusion
Findings
Discussions
Conclusion
Discussions
Findings
Background & related Information
Background & related Information
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Bhav Poochiye
Pricing Module developed
for selected commodities
for selected markets
for selected areas
DEMO
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Building Usage Profiles
Estimate access probabilities based on:
• Current user/community navigational patterns over site contents
(in the form of click streams)
• Historical user/community access patterns over site contents
(in the form of association rules)
Cluster needs based onlocation, income/age of user, time-of-day
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Data Mining
From datato information
to knowledge
to money!