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![Page 1: Page Rank Overview Two dimensional arrays Monte Carlo algorithms Searching the world wide web Big data Page rank Goal: we will write a program to compute.](https://reader038.fdocuments.in/reader038/viewer/2022103015/5519b6b95503466f578b4800/html5/thumbnails/1.jpg)
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Page Rank
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Overview
• Two dimensional arrays• Monte Carlo algorithms• Searching the world wide web• Big data• Page rankGoal: we will write a program to compute the relevancy of WWW documents based on the static structure of the WWW.
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Two Dimensional Arrays
• Significance (a topic on the AP Computer Science A exam)• Syntax• Example of matrix multiplication• Arrays of arrays
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Significance of Two Dimensional Arrays• Tables; for instance, assignments for each student in a class, quarterly
sales for each item in inventory, etc.• Matrices and binary relations in mathematics. For example, is there a
direct road from city1 in USA to city2 in USA?• For our goal in the this section, we will have need for the number of
links from doc1 in the WWW to doc2 in the WWW.
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Syntax
• int[][] frequency = new int [26][26];• Elements are accessed: frequency[4][7] and not frequency[4,7]• Array indices in Java (like C, C++, C#) always begin with 0; in other
words, the element with index 1 is the second element of the array.
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Matrix multiplication
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Matrix Multiplication Exercise
• http://cs.fit.edu/~ryan/java/programs/basic_algorithms/MatrixMultiplication2.java
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Arrays of Arrays
• Logically: arrays of arrays in the tradition of C and C++. Very simple.• Unfortunately: introduces pointers, memory allocation, etc. Very
complicated.
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Monte Carlo Methods
• Introduction• The example of a Monte Carlo estimate for Pi (Java exercise). Fair
shuffling (Java exercise). Random walk (important in financial analysis)• Used in path tracing to create realistic images• Percolation – an example of the power of a Monte Carlo algorithmGoal: we will write a Monte Carlo algorithm to estimate the relevancy of WWW documents based on the static structure of the WWW.
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Monte Carlo Casino
• The name refers to the grand casino in the Principality of Monaco at Monte Carlo, which is well-known around the world as an icon of gambling.
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Monte Carlo estimate for PiJava exercise: http://cs.fit.edu/~ryan/java/programs/basic_algorithms/ComputePi2.java
Since we know the value of pi it is not really necessary to invent an algorithm to estimate its value.
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Fair shuffling (Java exercise)
• How would you test a algorithm for shuffling, say, cards? In particular how would you know if all of the many possible results are equally likely?• Main program
http://cs.fit.edu/~ryan/java/programs/basic_algorithms/Experiment.java. Nothing to write; requires the method to shuffle.• http://cs.fit.edu/~ryan/java/programs/basic_algorithms/Shuffle.java
contains two methods of shuffling cards.• Run the experiment with multiple trials and convince yourself both
methods are fair
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Percolation Theory
Percolation. Pour liquid on top of some porous material.Will liquid reach the bottom? Many applications in chemistry, materials science, etc.• Spread of forest fires.• Natural gas through semi-porous rock.• Flow of electricity through network of resistors.• Permeation of gas in coal mine through a gas mask filter.
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15
Percolation TheoryGiven an N-by-N system where each site is vacant with probability p, what is the probability that system percolates?
Remark. Famous open question in statistical physics. No known mathematical solution. Computational thinking creates new science.Recourse. Take a computational approach: Monte Carlo simulation.Uses a recursive, dfs algorithm, but diverges from the present topic. (Recursion is a topic on the AP Computer Science A exam.)
p = 0.3(does not percolate)
p = 0.4(does not percolate)
p = 0.5(does not percolate)
p = 0.6(percolates)
p = 0.7(percolates)
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We will examine a Monte Carlo algorithm for estimating the relevancy of WWW documents.
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Random Walk
• Page rank can be computed a lot like random walk• See the Java applet (1 dim) at
http://www.math.uah.edu/stat/applets/RandomWalkExperiment.html• See the Java applet (2 dim) at
http://vlab.infotech.monash.edu.au/simulations/swarms/random-walk/
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Searching the World Wide Web
• History of Search Engines• Hypertext• Crawling the World Wide Web• Indexing
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History of Search Engines
• History of Search by Larry Kim of WordStream
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Markup and Hypertext
• Documents served up through the WWW are generally “marked up” for presentation in a structured, standard called hypertext markup language (HTML).• The most important feature of HTML is the referencing (via URLs) of
other WWW documents which enables easy, non-sequential, and varied paths of reading the documents.
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Hypertext
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WWW Spiders
• Google, and others, continually, crawl around the WWW recording what they see to enable searching.
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44% of hits and 35% of bandwidth is attributable to bots (and other odd things).
July 2013 (up to 9:30 am 26 Jul 2013) on the WWW server cs.fit.edu
Russian search engine
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Indexing
• Finding a relevant document is a vast ocean of linked HTML documents requires a very large index.• An index is a (sorted) list of keywords (terms) and the list of values
(URLs) which contain them.
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An example index of WWW documentsBourgeois .../manifesto.txtHero …/lilwomen.txt, …/muchado.txt, …/war+peace.txtHis .../manifesto.txt, …/lilwomen.txt, …/mobydick.txt, …/muchado.txt, …/war+peace.txtTreachery …/war+peace.txtWhale …/mobydick.txtYellowish …/lilwomen.txt , …/war+peace.txt
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Several Issues
• Pick out the words from the mark-up• What’s a word? 2nd, abc’s, CSTA• Normalize: lowercase, stemming• Some words are not worth indexing• “the”, “a”, etc.• A so-called stop list, eg., words ignored in Wikipedia search• Java exercise: http://cs.fit.edu/~ryan/java/programs/xml/URLtoText.java
First some preliminary remarks before doing the exercise.
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Searching and Sorting
Problem: Determine if the word is in the stop list. What is the best approach?
• Searching: linear search, binary search. (These are topics on the AP Computer Science A exam.) Binary search requires the data (the index, for example) to be sorted.• Sorting: selection sort, insertion sort, merge sort, quick sort; external
sorting. (The first three of these sorts are topics on the AP Computer Science A exam.)
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Linear versus Binary searchSuppose each
comparison takes one
millisecond (0.001)
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Linear versus Binary Search
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Linear versus Binary Search
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Obama at Google
• https://www.youtube.com/watch?v=k4RRi_ntQc8
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Sorting Demo
• http://cs.fit.edu/~ryan/cse1002/sort.html• See also sorting illustrated by Algo-rythmics
http://algo-rythmics.ms.sapientia.ro and folk dancers
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Now do the exercise
• Java exercise: http://cs.fit.edu/~ryan/java/programs/xml/URLtoText.java
• PS. How to students really program?• http://xkcd.com/1185 Observe the tool tip!
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OK, we have a keyword index. It is likely we still have “gazillion” documents, for most of the terms. (See Googlewacks, Googlewhackblatt; one and two words search terms that return one document.)How do we find the most relevant pages?
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Big Data
• The problem• Count-Min Algorithm
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The problem with Big Data
Consider a popular website which wants to keep track of statistics on the queries used to search the site. One could keep track of the full log of queries, and answer exactly the frequency of any search query at the site. However, the log can quickly become very large. This problem is an instance of the count tracking problem. Even known sophisticated solutions for fast querying such as a tree-structure or hash table to count up the multiple occurrences of the same query, can prove to be slow and wasteful of resources. Notice that in this scenario, we can tolerate a little imprecision. In general, we are interested only in the queries that are asked frequently. So it is acceptable if there is some fuzziness in the counts. Thus, we can tradeoff some precision in the answers for a more efficient and lightweight solution. This tradeoff is at the heart of sketches.Cormode and Muthurishnon, 2011
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Page Rank
• Gave Google a Competitive Advantage• Not based on the WWW surfer as voter (popularity), but on the
WWW author as voter (hence relatively static)• Random surfer mindlessly follows the hyperlinks of the WWW authors• Markov chains
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S&W Tiny Hypertext
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S&W Tiny Graph
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S&W Tiny: Adj list & Adj matrix
5
0 1
1 2 1 2
1 3 1 3 1 4
2 3
3 0
4 0 4 2
5 5 0 1 0 0 0 0 0 2 2 1 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0
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0 1 2 3 40
0.05
0.1
0.15
0.2
0.25
0.3
PAGE RANK TINY
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Wiki2 Hypertext
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Wiki2 Graph
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Wiki2: Adj List & Adj Matrix
70 1 0 2 0 3 0 4 0 61 02 0 2 13 1 3 2 3 44 0 4 2 4 3 4 55 0 5 46 4
7 70 1 1 1 1 0 11 0 0 0 0 0 01 1 0 0 0 0 00 1 1 0 1 0 01 0 1 1 0 1 0 1 0 0 0 1 0 00 0 0 0 1 0 0
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0 1 2 3 4 5 60
0.05
0.1
0.15
0.2
0.25
0.3
0.35
PAGE RANK WIKI2
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Wiki1 Hypertext
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Wiki1 Graph
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1 2 3 4 5 6 7 8 9 100
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
PAGE RANK WIKI1
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Java Exercise
• Modify Adajency1.java1. Print adjacency matrix2. Print probability matrix3. Print probability matrix with 90-10 rule
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Interactive WWW Page for PageRank
• http://williamcotton.com/pagerank-explained-with-javascript
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Reachability, Markov Theory
Can node 2 reach node 4? Yes, using a path of length 2 through node 3.
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Final Challenge
• Raise the page rank of page “23” by modifying only the links on page “23”• Decrease the page rank of page “23” by modifying only the links on
page “23”• Can you find the maximum/minimum page rank?
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Search engine optimization, link schemes, link farming, Google bombs
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Ted Talks: Brin & Page: The Genesis of Google• http://www.ted.com/talks/
sergey_brin_and_larry_page_on_google.html