CHAPTER 1: INTRODUCTION

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Green Island (Coral Cay Great Barrier Reef
Australia 9/18)
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Agin Court Reef (Outer Barrier Reef Australia
9/19)
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Search Engine

• A search engine is essentially a text retrieval
  system for web pages plus a Web interface.
• So whats new???

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Some Characteristics of the Web

• Web pages are
• very voluminous and diversified
• widely distributed on many servers.
• extremely dynamic/volatile.
• Web pages have
• more structure (extensively tagged).
• are extensively linked.
• may often have other associated metadata
• Web search is
• Noisy (pages with high similarity to query may
  still differ in relevance)
• Adversarial!
• A page can advertise itself falsely just so it
  will be retrieved
• Web users are
• ordinary folks (dolts?) without special
  training
• they tend to submit short queries.
• There is a very large user community.

Need to crawl and maintain index
Use the links and tags and Meta-data!
Use the social structure of the web
Easily impressed ?
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Crawlers Main issues

• General-purpose crawling
• Context specific crawiling
• Building topic-specific search engines

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SPIDER CASE STUDY
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Web Crawling (Search) Strategy

• Starting location(s)
• Traversal order
• Depth first
• Breadth first
• Or ???
• Cycles?
• Coverage?
• Load?

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Robot (2)

• Some specific issues
• What initial URLs to use?
• Choice depends on type of search engines to be
  built.
• For general-purpose search engines, use URLs that
  are likely to reach a large portion of the Web
  such as the Yahoo home page.
• For local search engines covering one or several
  organizations, use URLs of the home pages of
  these organizations. In addition, use appropriate
  domain constraint.

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Robot (7)

• Several research issues about robots
• Fetching more important pages first with limited
  resources.
• Can use measures of page importance
• Fetching web pages in a specified subject area
  such as movies and sports for creating
  domain-specific search engines.
• Focused crawling
• Efficient re-fetch of web pages to keep web page
  index up-to-date.
• Keeping track of change rate of a page

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Storing Summaries

• Cant store complete page text
• Whole WWW doesnt fit on any server
• Stop Words
• Stemming
• What (compact) summary should be stored?
• Per URL
• Title, snippet
• Per Word
• URL, word number

But, look at Googles Cache copy ..and its
privacy violations
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Robot (4)

• How to extract URLs from a web page?
• Need to identify all possible tags and attributes
  that hold URLs.
• Anchor tag lta hrefURL gt lt/agt
• Option tag ltoption valueURLgt lt/optiongt
• Map ltarea hrefURL gt
• Frame ltframe srcURL gt
• Link to an image ltimg srcURL gt
• Relative path vs. absolute path ltbase href gt

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Mercators way of maintaining URL
frontier ?Extracted URLs enter front queue ?Each
URL goes into a front queue based on
its Priority. (priority assigned Based on page
importance and Change rate) ?URLs are shifted
from Front to back queues. Each Back queue
corresponds To a single host. Each queue Has time
te at which the host Can be hit again ?URLs
removed from back Queue when crawler wants A page
to crawl
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Focused Crawling

• Classifier Is crawled page P relevant to the
  topic?
• Algorithm that maps page to relevant/irrelevant
• Semi-automatic
• Based on page vicinity..
• Distilleris crawled page P likely to lead to
  relevant pages?
• Algorithm that maps page to likely/unlikely
• Could be just A/H computation, and taking HUBS
• Distiller determines the priority of following
  links off of P

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Large Scale Indexing
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Map-Reduce Parallelism
(added after classbased on class discussion)

• Named after lisp constructs map and reduce
• (reduce fn2 (map fn1 list))
• Run function fn1 on every item of the list, and
  reduce the resulting list using fn2
• (reduce (map 1 (4 5 6 7 8 9)))
• (reduce (5 6 7 8 9 10))
• 151200 (5678910)
• (reduce (map primality-test (num1 num2)))
• So where is the parallelism?
• All the map operations can be done in parallel
  (e.g. you can test the primality of each of the
  numbers in parallel).
• The overall reduce operation has to be done after
  the map operation (but can also be parallelized
  e.g. assuming the primality-test returns a 0 or
  1, the reduce operation can partition the list
  into k smaller lists and add the elements of each
  of the lists in parallel (and add the results)
• Note that the parallelism in both the above
  examples depends on the length of input (the
  larger the input list the more parallel
  operations you can do in theory).
• Map-reduce on clusters of computers involve
  writing your task in a map-reduce form
• The cluster computing infrastructure will then
  parallelize the map and reduce parts using the
  available pool of machines (you dont need to
  thinkwhile writing the programas to how many
  machines and which specific machines are used to
  do the parallel tasks)
• An open source environment that provides such an
  infrastructure is Hadoop
• http//hadoop.apache.org/core/

Qn Can we bring map-reduce parallelism to
indexing?
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Partition the set of documents into blocks
construct index for each block separately
merge the indexes
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Distributing indexes over hosts

• At web scale, the entire inverted index cant be
  held on a single host.
• How to distribute?
• Split the index by terms
• Split the index by documents
• Preferred method is to split it by docs (!)
• Each index only points to docs in a specific
  barrel
• Different strategies for assigning docs to
  barrels

• At retrieval time
• Compute top-k docs from each barrel
• Merge the top-k lists to generate the final top-k
• Result merging can be tricky..so try to punt it
• Idea
• Consider putting most important docs in top few
  barrels
• This way, we can ignore worrying about other
  barrels unless the top barrels dont return
  enough results
• Another idea
• Split the top 20 and bottom 80 of the doc
  occurrences into different indexes..
• Short vs. long barrels
• Do search on short ones first and then go to long
  ones as needed

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Dynamic Indexing
simplest approach
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IR for Web Pages
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Use of Tag Information (1)

• Web pages are mostly HTML documents (for now).
• HTML tags allow the author of a web page to
• Control the display of page contents on the Web.
• Express their emphases on different parts of the
  page.
• HTML tags provide additional information about
  the contents of a web page.
• Can we make use of the tag information to improve
  the effectiveness of a search engine?

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Use of Tag Information (2)
Document is indexed not just with its contents
But with the contents of others descriptions of
it

• Two main ideas of using tags
• Associate different importance to term
  occurrences in different tags.
• Use anchor text to index referenced documents.

Page 2 http//travelocity.com/
Page 1
. . . . . . airplane ticket and
hotel . . . . . .
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Google Bombs The other side of Anchor Text
Document is indexed not just with its contents
But with the contents of others descriptions of
it

• You can tar someones page just by linking to
  them with some damning anchor text
• If the anchor text is unique enough, then even a
  few pages linking with that keyword will make
  sure the page comes up high
• E.g. link your SOs page with
• my cuddlybubbly woogums
• Shmoopie unfortunately is already taken by
  Seinfeld
• For more common-place keywords (such as
  unelectable or my sweet heart) you need a lot
  more links
• Which, in the case of the later, may defeat the
  purpose

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Use of Tag Information (3)

• Many search engines are using tags to improve
  retrieval effectiveness.
• Associating different importance to term
  occurrences is used in Altavista, HotBot, Yahoo,
  Lycos, LASER, SIBRIS.
• WWWW and Google use terms in anchor tags to index
  a referenced page.
• Qn what should be the exact weights for
  different kinds of terms?

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Use of Tag Information (4)

• The Webor Method (Cutler 97, Cutler 99)
• Partition HTML tags into six ordered classes
• title, header, list, strong, anchor, plain
• Extend the term frequency value of a term in a
  document into a term frequency vector (TFV).
• Suppose term t appears in the ith class tfi
  times, i 1..6. Then TFV (tf1, tf2, tf3, tf4,
  tf5, tf6).
• Example If for page p, term binghamton appears
  1 time in the title, 2 times in the headers and 8
  times in the anchors of hyperlinks pointing to p,
  then for this term in p
• TFV (1, 2, 0, 0, 8, 0).

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Use of Tag Information (6)

• The Webor Method (Continued)
• Challenge How to find the (optimal) CIV (civ1,
  civ2, civ3, civ4, civ5, civ6) such that the
  retrieval performance can be improved the most?
• One Solution Find the optimal CIV experimentally
  using a hill-climbing search in the space of CIV

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Use of LINK information Why?

• Pure query similarity will be unable to pinpoint
  right pages because of the sheer volume of pages
• There may be too many pages that have same
  keyword similarity with the query
• The even if you are one in a million, there are
  still 300 more like you phenomenon
• Web content creators are autonomous/uncontrolled
• No one stops me from making a page and writing on
  it this is the homepage of President Bush
• and adversarial
• I may intentionally create pages with keywords
  just to drive traffic to my page
• I might even use spoofing techniques to show one
  face to the search engine and another to the user
• So we need some metrics about the
  trustworthiness/importance of the page
• Lets look at social networks, since these topics
  have been investigated there..

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Connection to Citation Analysis

• Mirror mirror on the wall, who is the biggest
  Computer Scientist of them all?
• The guy who wrote the most papers
• That are considered important by most people
• By citing them in their own papers
• Science Citation Index
• Should I write survey papers or original papers?

Infometrics Bibliometrics
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What Citation Index says About Raos papers
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Scholar.google