WEB SEARCH and P2P

1
WEB SEARCH and P2P

• Advisor Dr Sushil Prasad
• Presented By DM Rasanjalee Himali

2
OUTLINE

• Introduction to web search engines
• What is a web search engine?
• Web Search engine architecture
• How a web search engine work?
• Relevance and Ranking
• Limitations in current Web Search Engines
• P2P Web Search Engines
• YouSearch
• Copeer
• ODISSEA
• Conclusion

3
What is a web search engine?

• A Web search engine is a search engine designed
  to search for information on the World Wide Web.
• Information may consist of web pages, images and
  other types of files.
• Some search engines also mine data available in
  newsgroups, databases, or open directories

4
History

Company Millions of searches Relative market share
Google 28,454 46.47
Yahoo! 10,505 17.16
Baidu 8,428 13.76
Microsoft 7,880 12.87
NHN 2,882 4.71
eBay 2,428 3.9
Time Warner 1,062 1.6
Ask.com 728 1.1
Yandex 566 0.9
Alibaba.com 531 0.8
Total 61,221 100.0

• Before there were search engines there was a
  complete list of all webservers.
• The very first tool used for searching on the
  Internet was Archie
• downloaded directory listings of files on FTP
  sites
• did not index the contents of these sites
• Soon after, many search engines appeared
• Excite, Infoseek, Northern Light, AltaVista.
  Yahoo!, Google, MSN Search

5
How Web Search Engine Work

• A search engine operates, in the following order
• Web crawling
• Indexing
• Searching

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Web Crawling

• A web crawler
• a program or which browses the World Wide Web in
  a methodical, automated manner.
• a means of providing up-to-date data
• create a copy of all the visited pages for later
  processing by a search engine
• starts with a list of URLs to visit, called the
  seeds.
• As the crawler visits these URLs, it identifies
  all the hyperlinks in the page and adds them to
  the list of URLs to visit, called the crawl
  frontier.
• URLs from the frontier are recursively visited
  according to a set of policies.

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Robot Exclusion Protocol

• also known as the robots.txt protocol
• is a convention to prevent cooperating web robots
  from accessing all or part of a website which is
  otherwise publicly viewable.
• User-agent
• Disallow /cgi-bin/
• Disallow /images/
• Disallow /tmp/
• Disallow /private/
• Sitemap http//www.example.com/sitemap.xml.gz
• Crawl-delay 10
• Allow /folder1/myfile.html
• Request-rate 1/5 maximum rate is one page
  every 5 seconds
• Visit-time 0600-0845 only visit between
  0600 and 0845 UTC (GMT)
• It relies on the cooperation of the web robot, so
  that marking an area of a site out of bounds with
  robots.txt does not guarantee privacy.
• The standard complements Sitemaps, a robot
  inclusion standard for websites.

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SiteMap Protocol

• allows a webmaster to inform search engines about
  URLs on a website that are available for
  crawling.
• A Sitemap is an XML file that lists the URLs for
  a site.
• It allows webmasters to include additional
  information about each URL
• when it was last updated,
• how often it changes, and
• how important it is in relation to other URLs in
  the site.
• This allows search engines to crawl the site more
  intelligently.
• Sitemaps are a URL inclusion protocol complement
  robots.txt

lt?xml version"1.0" encoding"UTF-8"?gt lturlset
xmlns"http//www.sitemaps.org/schemas/sitemap/0.9
"gt     lturlgt        ltlocgthttp//www.example.com/lt
/locgt       ltlastmodgt2005-01-01lt/lastmodgt
       ltchangefreqgtmonthlylt/changefreqgt
       ltprioritygt0.8lt/prioritygt     lt/urlgt
lt/urlsetgt
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Distributed Web Crawling

• Internet search engines employ many computers to
  index the Internet via web crawling.
• Dynamic assignment
• a central server assigns new URLs to different
  crawlers dynamically.
• allows the central server to, dynamically balance
  the load of each crawler.
• Static assignment
• there is a fixed rule stated from the beginning
  of the crawl that defines how to assign new URLs
  to the crawlers.
• Google uses thousands of individual computers in
  multiple locations to crawl the Web.

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Indexing

• The purpose of storing an index is to optimize
  speed and performance in finding relevant
  documents for a search query
• Search engine indexing collects, parses, and
  stores data to facilitate fast and accurate
  information retrieval.
• The contents of each page are analyzed to
  determine how it should be indexed
• Ex words are extracted from the titles,
  headings, or special fields called meta tags
• Meta search engines reuse the indices of other
  services and do not store a local index

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Challenges in Parallelism

• A major challenge in the design of search engines
  is the management of parallel computing
  processes.
• There are many opportunities for race conditions
  and coherent faults.
• Ex a new document is added to the corpus and the
  index must be updated, but the index
  simultaneously needs to continue responding to
  search queries.
• the search engine's architecture may involve
  distributed computing, where the search engine
  consists of several machines operating in unison.
  
• This increases the possibilities for incoherency
  and makes it more difficult to maintain a
  fully-synchronized, distributed, parallel
  architecture.

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Inverted Indices

• inverted index stores a list of the documents
  containing each word
• search engine can use direct access to find the
  documents associated with each word in the query
  to retrieve the matching documents quickly

Word Documents
the Document 1, Document 3, Document 4, Document 5
cow Document 2, Document 3, Document 4
says Document 5
moo Document 7
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Searching

• web search query
• a query that a user enters into web search engine
  to satisfy his or her information needs.
• is distinctive in that it is unstructured and
  often ambiguous
• vary greatly from standard query languages which
  are governed by strict syntax rules.

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Searching

• Three broad categories that cover most web search
  queries
• Informational queries
• Queries that cover a broad topic (e.g., colorado
  or trucks) for which there may be thousands of
  relevant results.
• Navigational queries
• Queries that seek a single website or web page of
  a single entity (e.g., youtube or delta
  airlines).
• Transactional queries
• Queries that reflect the intent of the user to
  perform a particular action, like purchasing a
  car or downloading a screen saver.

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Web search engine architecture
Fetched pages
URL List
Compress store
Anchors file
read

• Relative URLs
• ? absolute URLs
• ? docIDs

links
- Read repository - Uncompress parse docs to
hit list - Distribute hit list to baralles by
docID - Parse out links and store in anchor file
Anchor text --docIDs
Partiall sorted forward index
Resort baralls by word IDs
lexicon
Inverted index
Calculate PR of all docs
Answer queries
PR
From The Anatomy of a Large-Scale
Hypertextual Web Search Engine Sergey Brin and
Lawrence Page
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Important Properties Of Commercial Web Search

• To be successful a commercial Search Engine must
  address all of these issues/properties
• Millions of heterogeneous users
• Goal is to make money
• UI is extremely important
• Real-time/fast expectation
• Content of web page not sufficient to imply
  meaning
• Result ranking cannot assume independence
• Must consider maliciousness
• No quality control on pages (quality varies)
• Web is large (practically infinite)
• Millions of heterogeneous users

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Relevance and Ranking

• Exactly how a particular search engine's
  algorithm works is a closely-kept trade secret.
• However, all major search engines follow the
  general rules below.
• Location, Location, Location...and Frequency
• Location
• Search engines will also check to see if the
  search keywords appear near the top of a web
  page, such as in the headline or in the first few
  paragraphs of text. They assume that any page
  relevant to the topic will mention those words
  right from the beginning.
• Frequency
• A search engine will analyze how often keywords
  appear in relation to other words in a web page.
  Those with a higher frequency are often deemed
  more relevant than other web pages.

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Precision and Recall

• two widely used measures for evaluating the
  quality of results in Information Retrieval
• Precision
• fraction of the documents retrieved that are
  relevant to the user's information need
• number of relevant documents retrieved by a
  search ___________________________________________
  ________ the total number of documents retrieved
  by that search
• Recall
• the fraction of the documents that are relevant
  to the query that are successfully retrieved.
• number of relevant documents retrieved by a
  search ___________________________________________
  __________ the total number of existing relevant
  documents which should have been retrieved
• Often, there is an inverse relationship between
  Precision and Recall

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Relevance and Ranking

• webmasters constantly rewrite their web pages in
  an attempt to gain better rankings.
• Some sophisticated webmasters may even "reverse
  engineer" the location/frequency systems used by
  a particular search engine
• Because of this, all major search engines now
  also make use of "off the page" ranking criteria

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Relevance and Ranking

• Off the page factors
• those that a webmasters cannot easily influence
• Link analysis
• Search engine analyzing how pages link to each
  other
• Helps to determine what a page is about and
  whether that page is "important" and thus
  deserving of a ranking boost
• Click through measurement
• a search engine watch what results someone
  selects for a particular search,
• eventually drop high-ranking pages that aren't
  attracting clicks,
• promote lower-ranking pages that do pull in
  visitors.

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Limitations in current web search engines

• Centralized search engines have limited
  scalability.
• crawler based indices are stale and incomplete
• Fundamental issue How much of the web is
  crawlable
• If you follow the rules many sites say robots
  get lost
• What about Dynamic content? (Deep Web)
• The deep web is around 500 times larger than
  surface web. These deep web resources mainly
  include data held by databases which can be
  accessed only through queries. Since crawlers
  discover resources only through links, they
  cannot discover these resources.
• Theres no guarantee that current search engines
  index or even crawl the total surface web space

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Limitations in current web search engines

• Single point of failure
• Ambiguous words
• Polysemy - words with multiple meanings train
  car train neural network
• Synonymy - multiple words same meaning neural
  network is trained as follows neural network
  learns as follows
• What about phrases - searches are not bag of
  words
• Positional information? Structural (throw out
  case punctuation)?
• Non-text content data worth storing
• Most web search engines today crawl only surface
  web.

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P2P Web Search

• Seen explosion of activity in the area of
  peer-to-peer (P2P) systems last few years
• Since an increasing amount of content now resides
  in P2P networks, it becomes necessary to provide
  search facilities within P2P networks.
• The significant computing resources provided by a
  P2P system could also be used to implement search
  and data mining functions for content located
  outside the system
• e.g., for search and mining tasks across large
  intranets or global enterprises, or even to build
  a P2P-based alternative to the current major
  search engines.

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P2P Web Search

• The characteristics distinguish P2P systems from
  previous technologies
• low maintenance overhead
• improved scalability
• Improved reliability
• synergistic performance
• increased autonomy and privacy
• Dynamism

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P2P Web Search Engines

• YouSearch
• Coopeer
• ODISSEA

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YouSearch

• YouSearch
• is a distributed search application for personal
  webservers operating within a shared context
• Allow peers to aggregate into groups and users to
  search over specific groups
• Goal
• Provide fast, fresh and complete results to users

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YouSearch

• System Overview
• participants in YouSearch
• Peer-nodes
• run YouSearch enabled clients
• Browsers
• search YouSearch enabled content through their
  web browsers
• Registrar
• centralized light-weight service that
• acts like a blackboard on which peer nodes
  store and lookup (summarized) network state.

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YouSearch

• System Overview
• Search System
• Each peer node closely monitors its own content
  to maintain a fresh local index
• A bloom filter content summary is created by each
  peer and pushed to the registrar.
• When a browser issues a search query at a peer p
  , the peer p first queries the summaries at the
  registrar to obtain a set of peers R in the
  network that are hosting relevant documents.
• The peers in R are then directly contacted by
  with the query to obtain the URLs for its
  results.
• To quickly satisfy any subsequently issued
  queries with identical terms, the results from
  each query issued at a peer p are cached for a
  limited time at p

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YouSearch

• Indexing
• Indexing is periodically executed at every peer
  node.
• Inspector examines each shared file for its last
  modification date and time.
• If the file is new or the file has changed, the
  file is passed to the Indexer.
• The Indexer maintains a disk-based inverted-index
  over the shared content.
• The name and path information of the file are
  indexed as well.

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YouSearch

• Indexing
• Summarizer
• The Summarizer obtains a list of terms T from the
  Indexer and creates a bloom filter from them in
  the following way.
• A bit vector V of length L is created with each
  bit set to 0.
• A specified hash function H with range 1,...,L
  is used to hash each term t in T and the bit at
  position H(t) in V is set to 1
• YouSearch use k independent hash functions
  H1,H2,...,Hk and construct k different bloom
  filters, one for each hash function
• In YouSearch,
• the length of each bloom filter is L 64 Kbits
  and
• the number of bloom filters k is set to 3
• Summary Manager at the registrar aggregate these
  Bloom Filters into a structure that maps each bit
  position to a set of peers whose Bloom Filters
  have the corresponding bit set

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YouSearch

• Querying

query
query
computes the hash of keywords
keywords
determine
intersection of peer I s
looks up mapping
Corresponding bits of each k bloom filters
keywords
intersection of peer I s
results
Bit position to IP address mapping
contacts each of the peers in list and obtains a
list of URLs for matching documents
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YouSearch

• Caching
• Every time a global query is answered that
  returns non-zero results, the querying peer
  caches the result set of URLs U (temporary)
• The peer then informs the registrar of the fact.
• The registrar adds a mapping from the query to
  the IP-address of the caching peer in its cache
  table

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YouSearch

• Limitations
• False Positive results 17.38
• Central registrar gtgt single point of failure
• No extensive phrase search
• No attention has been given for query ranking
• No human user collaboration

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Coopeer

• Coopeer
• Is a P2P web search engine where each user
  computer stores a part of the web model used for
  indexing and retrieving web resources in response
  to queries
• Goal
• complement centralized search engines to provide
  more humanized and personalized results by
  utilizing users collaboration

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Coopeer

• (a)Collaboration
• One may look for interesting web pages in the P2P
  knowledge repository consisted with shared web
  pages.
• A novel collaborative filtering technique called
  PeerRank is presented to rank pages proportional
  to the votes from relevant peers
• (b)Humanization
• Coopeer use a query-based representation for
  documents,
• The relevant words are not directly extracted
  from page content but introduced by human users
  with a high proficiency in their expertise
  domains.
• (c)Personalization
• Similar users are self-organized according to
  their semantic content of search session.
• Thus, requestor peer can extend routing paths
  along its neighbors, rather than just take a
  blind shot.
• User-customized results can be obtained along
  personal routing paths in contrast with CSEs.

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Coopeer

• System Overview
• requestor forwards the query based on the
  semantically routing.
• Peers maintain a local index about the semantic
  content of remote peers.
• Receiving a query message from remote peer,
  current peer check it against the local store.
• In order to facilitate this work, a novel
  query-based representation about documents is
  introduced.
• Based on query representation, cosine similarity
  between new query and documents can be computed.
• the documents are relevant enough, if the
  similarity exceeds a certain threshold.
• Then these results are returned to the requestor.
• Receiving the returned results, the requestor
  peer need to rank them in term of preference of
  its human owner using PeerRank method.

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Coopeer

• The Coopeer client consists of four main software
  agents
• The User Agent
• is responsible for interacting with the users.
• It provides a friendly user interface, so that
  users can conveniently manage and manipulate the
  whole search sessions.
• The Web-searcher Agent
• is the resource of P2P knowledge repository.
• It performs the users individual searching with
  several search engines from the Internet.
• The Collaborator Agent
• is the key component for performing users
  real-time collaborative searching.
• It facilitates maintaining the P2P knowledge
  repository, such as information sharing,
  searching, and fusion.
• The Manager Agent
• is the key component of Coopeer, which
  coordinates and manage the other types of agents.
  
• It is also responsible for updating and
  maintaining data.

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Coopeer

• PeerRank
• All the users are taken as a Referrer Network.
• Determines pages relevance by examining a
  radiating network of referrers.
• Documents with more referrers gain higher ranks.
• Obtain better rank order, as collaborative
  evaluation of human users is much more precise
  than description of term frequency or link
  amount.
• Prevent spam, since it is difficult to pretend
  evaluation from human users.

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Coopeer

• PeerRank
• For a given search session, we firstly compute
  the similarity between requestors favorite lists
  and referrers,
• then the similarity is used as the baseline of
  recommending degree of the referrer.
• Firstly, as shown in equation (1), the similarity
  of local list and recommended list is given by
  the Kendall measure.
• Secondly, we convert the rank of a given URL in
  its recommended list to a moderate score
• R(e) - weight of URL e.
• C (e) - set constituted by es referrers.
• Z - constant gt 1.
• p - local peer
• Pi - a remote peer,
• Lp , Lpi - list of p and Pi respectively.
• K(r)(Lp, Lpi ) -Kendall function to measure the
  distance of the local list and the recommended
  list,
• r decay factor.
• SLpi(e) - score of e in the recommended list.
• Re - rank of e and
• RMax - highest rank of list pi, the length of
  the list.

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Coopeer

• Kendall Measure
• Kendall is used to measure the distance between
  two lists in the same length.
• Paper extend it to fit in with measuring two
  lists in different length.
• Kendall function

• t1 and t2 - two lists composed with URLs
• Kr(t1, t2) -the distance between t1 and t2,
• r fixed parameter with 0 r 1. C2
• 2L - used for normalization is the possible
  maximum of the distance.
• U(t1, t2) - set consists of all the URLs in t1
  and t2,
• K ri,j(t1, t2) - means the penalty of the URL
  pair (i, j)

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Coopeer

• Query Based Representation
• A novel type of representation based on the
  relevant words introduced by human users with a
  high proficiency in their expertise domains.
• is efficient on the P2P platform, as the users
  evaluation can be utilized easily through the
  client application.
• represent and organize the local documents for
  responding remote query

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Coopeer

• Each peer maintains
• an inverted index table
• represent local documents for responding remote
  query
• the IDs of the documents that were replied the
  query
• key of inverted index is terms extracted from the
  previous queries
• Ex when peer j writes in two queries P2P
  Overlay and P2P Routing and obtains two set of
  documents, d1, d2, d3 and d3, d4
  respectively.
• The retrieved documents will be updated with
  their corresponding query terms.
• When any other peer issues a query about Overlay
  Routing Algorithm, peer j would look up relevant
  documents in the inverted index by using VSM
  cosine similarity as ranking algorithm, and d3
  would gain the highest ranking.

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Coopeer

• Semantic Routing Algorithm
• each Coopeer client maintains a local Topic
  Neighbor Index
• The index records the used performance of remote
  peers which has similar topics to the local peer.
• These search sessions queries are used to
  represent the peers semantic content

• session 1 gtgt is the local peer which has two
  topics (queries)
• other sessions below denote the remote peers are
  interested in by the local peer in some aspect.
• session 2 and 3 are relevant to P2P Routing
  topic of local peer, while others are about
  Pattern Recognition.
• The peers on a same topic are in descending order
  of the rate.
• The peers providing more interested resource
  would move to the top of an individuals local
  index

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Coopeer

• with query-based inverted index, the precision of
  matching results of different subjects was almost
  100
• system uses information coming from centralized
  search engines, so the system is not aimed to
  replace CSEs, but to complement them.

45
Coopeer

• Query based representation is Efficient in p2p
  because users evaluation can be utilized easily
  through the client application.
• This is Inefficient in CSEs because gaining user
  evaluation through web browser is inefficient
  impractical to store and index documents every
  users query.
• Prevent spam, since it is difficult to pretend
  evaluation from human users.
• Use human searching experience ?better results

46
ODISSEA

• A distributed global indexing and query execution
  service
• Maintains a global index structure under document
  insertions and updates and node joins and
  failures
• the inverted index for a particular term (word)
  is located at a single node, or partitioned over
  a small number of nodes in some hybrid
  organizations.
• Assume two tier architecture.
• The system is implemented on top of an underlying
  global address space provided by a DHT structure

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ODISSEA

• System provide the lower tier of the two tier
  architecture.
• In the upper tier, there are two classes of
  clients that interact with this P2P-based lower
  tier
• Update clients
• insert new or updated documents into the system,
  which stores and indexes them.
• An update client could be a crawler inserting
  crawled pages, a web server pushing documents
  into the index, or a node in a file sharing
  system.
• Query clients
• design optimized query execution plans, based on
  statistics about term frequencies and
  correlations, and issue them to the lower tier.

48
ODISSEA
49
ODISSEA

• Global Index
• An inverted index for a document collection is a
  data structure that contains for each word in the
  collection a list of all its occurrences, or a
  list of postings.
• Each posting contains the document ID of the
  occurrence of the word, its position inside the
  document, and other information (in title? bold
  face?)
• each node holds a complete global postings list
  for a subset of the words, as determined by a
  hash function.

50
ODISSEA

• Query Processing
• a ranking function is a function F that, given a
  query consisting of a set of search terms
  q0,q1,,qm-1 , assigns to each document d a score
  F(d, q0,q1,,qm-1) . The top- k ranking problem
  is then the problem of identifying the k
  documents in the collection with the highest
  scores.

51
ODISSEA

• We focus on two families of ranking functions,
• The first family includes the common families of
  term-based ranking functions used in IR, where we
  add up the scores of each document with respect
  to all words in the queries.
• The second formula adds a query-independent value
  g(d) to the score of each page

52
ODISSEA

• Fagins Algorithm
• Consider the inverted lists for a search query
  with two terms q0 and q1 .
• Assume they are located on the same machine, and
  that the postings in the list are pairs
  (d,f(d,qi)),i ?0,1, where d is an integer
  identifying the document and " f(d,qi) is real
  valued.
• Assume each inverted list is sorted by the second
  attribute, so that documents with largest "
  f(d,qi) are at the start of the list.
• Then the following algorithm, called FA, computes
  the top-k results

53
ODISSEA

• FA
• (1)Scan both lists from the beginning, by reading
  one element from each list in every step, until
  there are documents that have each been
  encountered in both of the lists.
• (2) Compute the scores of these documents. Also,
  for each document that was encountered in only
  one of the lists, perform a lookup into the other
  list to determine the score of the document.
  Return the documents with the highest score.

54
Conclusion

• Still no P2P web search engine has outperformed
  Google!
• () Lot of resources for complex data mining
  tasks and for crawling whole surface web
• ()Emergence of semantic communities also has a
  positive impact on p2p web search performance
• (-)lack of global knowledge
• (-)smart crawling strategies beyond BFS are hard
  to implement in a P2P environment without a
  centralized scheduler.

55
Some Open Problems

• how to uniformly sample web pages on a web site
  if one does not have an exhaustive list of these
  pages?
• Bar-Yosseff converted the web graph into an
  undirected, connected, and regular graph.
• The equilibrium of a random walk on this graph is
  the uniform distribution.
• It is not clear how many steps such a walk needs
  to perform.
• A more significant problem, however, is that
  there is no reliable way of converting the web
  graph into an undirected graph.

56
Some Open Problems

• Data Streams
• The query logs of a web search engine contain all
  the queries issued at this search engine.
• The most frequent queries change only slowly over
  time.
• However, the queries with the largest increase or
  decrease from one time period over the next show
  interesting trends in user interests. We call
  them the top gainers and losers.
• Since the number of queries is huge, the top
  gainers and losers need to be computed by making
  only one pass over the query logs.
• This leads to the following data stream problem
• Another interesting variant is to find all items
  above a certain frequency whose relative increase
  (i.e., their increase divided by their frequency
  in the first sequence) is the largest.

57
References

• The anatomy of a large-scale hypertextual Web
  search engineSource Computer Networks and ISDN
  Systems Volume 30 ,  Issue 1-7  ,1998 Sergey Brin
  Lawrence Page
• Make it fresh, make it quick searching a network
  of personal International World Wide Web
  Conference Budapest, Hungary , 2003
• Towards a Fully Distributed P2P Web Search
  EngineProceedings of the 10th IEEE International
  Workshop on Future Trends of Distributed
  Computing Systems Jin Zhou, Kai Li and Li Tang
   2004
• Odissea A peer-to-peer architecture for scalable
  web search and information retrieval by T Suel,
  C Mathur, J Wu, J Zhang, A Delis, M Kharrazi, X
  Long, K Shanmugasunderam , 2003
• Space/time Trade-offs in Hash Coding with
  Allowable Errors B. Bloom. In Communications of
  ACM, volume 13(7), pages 422426, 1970
• www.en.wikipedia.org

58
Extra Slides
59
Bloom Filters

• a space-efficient probabilistic data structure
  that is used to test whether an element is a
  member of a set.
• False positives are possible, but false negatives
  are not.
• The more elements that are added to the set, the
  larger the probability of false positives.

60
Bloom Filters

• An empty Bloom filter is a bit array of m bits,
  all set to 0.
• There must also be k different hash functions
  defined, each of which maps a key value to one of
  the m array positions.
• To add an element, feed it to each of the k hash
  functions to get k array positions. Set the bits
  at all these positions to 1.
• To query for an element (test whether it is in
  the set), feed it to each of the k hash functions
  to get k array positions.
• If any of the bits at these positions are 0, the
  element is not in the set if it were, then all
  the bits would have been set to 1 when it was
  inserted.
• If all are 1, then either the element is in the
  set, or the bits have been set to 1 during the
  insertion of other elements.

61
Bloom Filters
An example of a Bloom filter, representing the
set x, y, z. The colored arrows show the
positions in the bit array that each set element
is mapped to. The element w, not in the set, is
detected as a nonmember as it is mapped to a
position containing a 0.
62
Bloom Filters