1. Tamer Elsayed, Jimmy Lin, and Douglas Oard
Niveda Krishnamoorthy

2.  PairwiseSimilarity
 MapReduce Framework
 Proposed algorithm
• Inverted Index Construction
• Pairwise document similarity calculation
 Results

3.  PubMed – “More like this”
 Similar blog posts
 Google – Similar pages

4.  Framework that supports distributed
computing on clusters of computers
 Introduced by Google in 2004
 Map step
 Reduce step
 Combine step (Optional)
 Applications

6.  Consider two files:
Hello Hello
Hello ,2
World Hadoop World ,2
Bye Goodbye Bye,1
Hadoop ,2
World Hadoop Goodbye ,1

7. Hello <Hello,1>
World <World,1>
Map 1
Bye <Bye,1>
World <World,1>
Hello <Hello,1>
Hadoop <Hadoop,1>
Map 2
Goodbye <Goodbye,1>
Hadoop <Hadoop,1>

8. <Hello,1>
S <Hello (1,1)> Reduce 1 Hello ,2
<World,1>
H
U
<Bye,1> <World(1,1)> Reduce 2 World ,2
F
F
<World,1>
L <Bye(1)> Reduce 3 Bye,1
E
<Hello,1> <Hadoop(1,1)> Reduce 4 Hadoop ,2
&
<Hadoop,1>
S <Goodbye(1)> Reduce 5 Goodbye ,1
<Goodbye,1> O
R
<Hadoop,1> T

9. MAPREDUCE ALGORITHM Scalable
•Inverted Index Computation and
•Pairwise Similarity Efficient

10. Document 1
A <A,(d1,2)>
A
B Map 1 <B,(d1,1)>
C
<C,(d1,1)>
Document 2
B <B,(d2,1)>
D
D Map 2
<D,(d2,2)>
Document 1 <A,(d3,1)>
A
B <B,(d3,2)>
Map 3
B
E <E,(d3,1)>

11. <A,(d1,2)>
S <A,[(d1,2), <A,[(d1,2),
<B,(d1,1)> H (d3,1)]> Reduce 1 (d3,1)]>
U
<C,(d1,1)> F <B,[(d1,1), (d2, <B,[(d1,1), (d2,
F Reduce 2
1),(d3,2)]> 1),(d3,2)]>
L
<B,(d2,1)> E <C,[(d1,1)]> Reduce 3 <C,[(d1,1)]>
<D,(d2,2)> &
<D,[(d2,2)]> Reduce 4 <D,[(d2,2)]>
S
<A,(d3,1)> O
R <E,[(d3,1)]> Reduce 5 <E,[(d3,1)]>
<B,(d3,2)> T
<E,(d3,1)>

12.  Group by document ID, not pairs
 Golomb’s compression for postings
 Individual Postings
 List of Postings

13. <(d1,d3),2>
<A,[(d1,2), Map 1
(d3,1)]>
<(d1,d2),1
<B,[(d1,1), (d2,
Map 2 (d2,d3),2
1),(d3,2)]>
(d1,d3),2>
<C,[(d1,1)]>
<D,[(d2,2)]>
<E,[(d3,1)]>

14. S
H
<(d1,d3),2> U
F <(d1,d2)[1]> <(d1,d2)[1]>
Reduce 1
F
<(d1,d2),1 L
E <(d2,d3)[2]> Reduce 2 <(d2,d3)[2]>
(d2,d3),2
(d1,d3),2>
&
Reduce 3
<(d1,d3)[2,2]> <(d1,d3)[4]>
S
O
R
T

15.  Hadoop 0.16.0
 20 machine (4GB memory, 100GB disk)
 Similarity function - BM25
 Dataset: AQUAINT-2 (newswire text)
• 2.5 GB
• 906k documents

16.  Tokenization
 Stop word removal
 Stemming
 Df-cut
• Fraction of terms with highest document
frequency is eliminated – 99% cut (9093)
Linear space and time complexity
• 3.7 billion pairs (vs) 81. trillion pairs

19.  Complexity: O(n2)
 Df-cut
of 99 percent eliminates meaning bearing
terms and some irrelevant terms
• Cornell, arthritis
• sleek, frail
 Df-cut can be relaxed to 99.9 percent

20.  Exact algorithms used for inverted index
construction and pair-wise document
similarity are not specified.
 Df-cut – Does a df-cut of 99 percent affect
the quality of the results significantly?
 The results have not been evaluated.