CS411: Summary and Beyond

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CS411Summary and Beyond

• Kevin C. Chang

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About the Final Exam

• Friday 12/17 130-430pm, 1320 DCL
• Closed book/note, only scratch paper allowed
• Coverage Accumulative, with emphasis on 2nd half
• I and TA will be here to help with questions
• Bring your UIUC ID
• Do not discuss exam questions/solutions on the
  newsgroup

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Midterm Format

• One set of true/false questions
• One set of short answer questions
• Followed by several more questions
• The question formats are similar to the questions
  we covered in the lectures and homeworks
• 100 points
• I believe you should have enough time

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Suggested Method for Study

• Go over the lecture slides
• Read the textbook
• Try to work out solutions to problems on
  hw/lectures before looking at the actual solution
• Work on sample exams
• before looking at solutions
• Discuss with people in your group

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Summary What you have learned?

• The user perspective
• How to use RDBMS and build database
  applications?
• Demo 1a The ASAP team, Friends Forest
• Demo 1b The Initech, Integrate 2XS

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Summary What you have learned?

• The system perspective
• How does RDBMS work?
• Demo 2 Preston Brown Jeffery Votteler
  PostgreSQL Plan Enumeration Visualizer

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Beyond Data management in the information age

• Information abounds in our civilization and
  inundates our daily life.
• Beyond database management
• Data management issues everywhere
• Demo 3 My research projects

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Todays Search Engine--

• Only keyword matching-- Guess what your target
  page will say
• e.g., to find Kevin Changs email
• Only individual pages -- Search does not go
  beyond one page
• e.g., to find Kevin Changs most likely email
  at UIUC
• e.g., to find all CS profs email
• Only follow links-- Databases remain untapped
  territory
• e.g., to find all flights to San Francisco
• e.g., to find all jobs in Urbana-Champaign

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Getting Structure Data from the Web Integration
and Mining

• Getting structured data from --
• The deep Web
• semantic-rich, structured data hidden deeply
  inside databases on the Web
• need integration to access these databases
• The surface Web
• semantic-rich, structured data hidden
  implicitly on the surface Web
• need mining to find these relations

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Project 1 MetaQuerier Knocking the Door to the
Deep Web
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The previous Web things are just on the surface
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The current Web Getting deeper with
non-trivial access
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How to enable effective access to the deep Web?
Cars.com
Amazon.com
Biography.com
Apartments.com
411localte.com
401carfinder.com
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Amy is a new graduate, just moving to her new
career

• Finding sources
• Wants to upgrade her car Where can she study for
  her options? (cars.com, edmunds.com)
• Wants to buy a house Where can she look for
  houses in her town? (realtor.com)
• Wants to write a grant proposal. (NSF Award
  Search)
• Wants to check for patents. (uspto.gov)
• Querying sources
• Then, she needs to learn the grueling details of
  querying

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MetaQuerier Exploring and integrating deep
Web

• Explorer
• source discovery
• source modeling
• source indexing

FIND sources
Amazon.com
Cars.com
db of dbs

• Integrator
• source selection
• schema integration
• query mediation

Apartments.com
QUERY sources
411localte.com
unified query interface
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Toward large scale integration

• We are facing very different large scale
  scenarios!
• Many sources on the Web, order of 105
• Such integration must be dynamic and ad-hoc
• Dynamic discovery
• Sources are dynamically changing
• On-the-fly integration
• Queries are ad-hoc and need different sources

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Our survey found SIGMOD-Record Sep04

• Challenge reassured
• 450,000 online databases
• 1,258,000 query interfaces
• 307,000 deep web sites
• 3-7 times increase in 4 years
• Insight revealed
• Web sources are not arbitrarily complex
• Amazon effect convergence and regularity
  naturally emerge

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Demo.
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Project 2 WISDM Uncovering Structured Data on
the Surface Web
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The WISDM goal
WISDM Web Indexing and Search for Data Mining
The Web
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Relation discovery Weaving entities into
relations
email
phone
prof
WISDM-ER
ltprof, phone, emailgt
dewitt_at_cs.wisc.edu
608-263-5489
David DeWitt
R1
winslett_at_cs.uiuc.edu
333-3536
Marianne Winslett
Entity-Relation Discovery



ltprof, univ, researchgt
R2
univ
research
prof
U. Wisconsin
database systems
David DeWitt
Purdue U.
data mining
Chris Clifton



The Web
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Example applicationsRelation is the essence
of many info search

• CSContact By weaving R1 ltprof, phone, emailgt
• What is the phone and email of, say, David
  DeWitt?
• What are the email of all profs at Wisconsin?
• CSResearch By weaving R2 ltprof, univ,
  researchgt
• What is the research area of DeWitt?
• Who are database professors at various
  universities?
• Which area has the most faculty at Wisconsin?

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Example applications Structured data can be
further processed

• By joining R1 with R2
• What are the emails of the database professors at
  Wisconsin?
• By joining R2 with a university ranking
  database
• Which top-20 university has the most database
  faculty?

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Current testbed A small corpus to peek the
potential

• Data pages 6 midwest CS departments
• Tagged entities prof, email, phone, univ,
  research, state

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Demo.
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So, thats the end of CS411 And hopefully the
start of your DATA career
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Thank You!
For more information http//www-faculty.cs.uiuc
.edu/kcchang/ kcchang_at_cs.uiuc.edu