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Inverted Indexing for Text Retrieval. Chapter 4 Lin and Dyer. Introduction. Web search is a quintessential large-data problem. So are any number of problems in genomics. Google, amazon ( aws ) all are involved in research and discovery in this area

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introduction
Introduction
  • Web search is a quintessential large-data problem.
  • So are any number of problems in genomics.
    • Google, amazon (aws) all are involved in research and discovery in this area
  • Web search or full text search depends on a data structure called inverted index.
  • Web search problem breaks down into three major components:
    • Gathering the web content (crawling) (like project 1)
    • Construction of inverted index (indexing)
    • Ranking the documents given a query (retrieval) (exam 1)
issues with these components
Issues with these components
  • Crawling and indexing have similar characteristics: resource consumption is high
      • Typically offline batch processing except of course on twitter model
  • There are many requirements for a web crawler or in general a data aggregator..
    • Etiquette, bandwidth resources, multilingual, duplicate contents, frequency of changes…
    • How often to collect: too few may miss important updates, too often may have too much info
web crawling
Web Crawling
  • Start with a “seed” URL , say wikipedia page, and start collecting the content by following the links in the seed page; the depth of traversal is also specified by the input
  • What are the issues?
  • See page 67
retrieval
Retrieval
  • Retrieval is a online problem that demands stringent timings: sub-second response times.
    • Concurrent queries
    • Query latency
    • Load on the servers
    • Other circumstances: day of the day
    • Resource consumption can be spikey or highly variable
  • Resource requirement for indexing is more predictable
indexes
Indexes
  • Regular index: Document  terms
  • Inverted index termdocuments
  • Example:

term1  {d1,p}, {d2, p}, {d23, p}

term2  {d2, p}. {d34, p}

term3  {d6, p}, {d56, p}, {d345, p}

Where d is the doc id, p is the payload (example for payload: term frequency… this can be blank too)

inverted index
Inverted Index
  • Inverted index consists of postings lists, one associated with each term that appears in the corpus.
  • <t, posting>n
  • <t, <docid, tf> >n
  • <t, <docid, tf, other info>>n
  • Key, value pair where the key is the term (word) and the value is the docid, followed by “payload”
  • Payload can be empty for simple index
  • Payload can be complex: provides such details as co-occurrences, additional linguistic processing, page rank of the doc, etc.
  • <t2, <d1, d4, d67, d89>>
  • <t3, <d4, d6, d7, d9, d22>>
  • Document numbering typically do not have semantic content but docs from the same corpus are numbered together or the numbers could be assigned based on page ranks.
retrieval1
Retrieval
  • Once the inverted index is developed, when a query comes in, retrieval involves fetching the appropriate docs.
  • The docs are ranked and top k docs are listed.
  • It is good to have the inverted index in memory.
  • If not , some queries may involve random disk access for decoding of postings.
  • Solution: organize the disk accesses so that random seeks are minimized.
pseudo code
Pseudo Code

Pseudo code  Baseline implementation  value-key conversion pattern implementation…

inverted index baseline implementation using mr
Inverted Index: Baseline Implementation using MR
  • Input to the mapper consists of docid and actual content.
  • Each document is analyzed and broken down into terms.
  • Processing pipeline assuming HTML docs:
      • Strip HTML tags
      • Strip Javascript code
      • Tokenize using a set of delimiters
      • Case fold
      • Remove stop words (a, an the…)
      • Remove domain-specific stop works
      • Stem different forms (..ing, ..ed…, dogs – dog)
baseline implementation
Baseline implementation

procedure map (docid n, doc d)

H  new Associative array

for all terms in doc d

H{t}  H{t} + 1

for all term in H

emit(term t, posting <n, H{t}>)

reducer for baseline implmentation
Reducer for baseline implmentation

procedure reducer( term t, postings[<n1, f1> <n2, f2>, …])

P  new List

for all posting <a,f> in postings

Append (P, <a,f>)

Sort (P) // sorted by docid

Emit (term t, postings P)

shuffle and sort phase
Shuffle and sort phase
  • Is a very large group by term of the postings
  • Lets look at a toy example
  • Fig. 4.3 some items are incorrect in the figure
baseline mr for ii
Baseline MR for II

class Mapper

procedure Map(docid n; doc d)

H =new AssociativeArray

for all term t in doc d do

H(t) H(t) + 1

for all term t in H do

Emit(term t; posting (n,H[t])

class Reducer

procedure Reduce(term t; postings [hn1; f1i; hn2; f2i : : :])

P = new List

for all posting (t,f) in postings [(n1,f1); (n2, f2) : : :] do

Append(P, (t, f))

Sort(P)

Emit(term t; postings P)

revised implementation
Revised Implementation
  • Issue: MR does not guarantee sorting order of the values.. Only by keys
  • So the sort in the reducer is an expensive operation esp. if the docs cannot be held in memory.
  • Lets check a revised solution
  • (term t, posting<docid, f>) to
  • (term<t,docid>, tf f)
inverted index revised implementation
Inverted Index: Revised implementation
  • From Baseline to an improved version
  • Observe the sort done by the Reducer. Is there any way to push this into the MR runtime?
  • Instead of
    • (term t, posting<docid, f>)
  • Emit
    • (tuple<t, docid>, tf f)
  • This is our previously studied value-key conversion design pattern
  • This switching ensures the keys arrive in order at the reducer
  • Small memory foot print; less buffer space needed at the reducer
  • See fig.4.4
modified mapper
Modified mapper

Map (docid n, doc d)

H  new AssociativeArray

For all terms t in doc

H{t}  H{t} + 1

For all terms in H

emit (tuple<t,n>, H{t})

modified reducer
Modified Reducer

Initialize

tprev 0

P  new PostingList

method reduce (tuple <t,n>, tf[f1, ..])

if t # tprev ^ tprev # 0

{ emit (term t, posting P);

reset P; }

P.add(<n,f>)

tprev  t

Close

emit(term t, postings P)

improved mr for ii
Improved MR for II

class Mapper

method Map(docid n; doc d)

H = new AssociativeArray

for all term t in doc d do

H[t] = H[t] + 1

for all term t in H do

Emit(tuple <t; n>, tfH[t])

class Reducer

method Initialize

tprev = 0; P = new PostingsList

method Reduce(tuple <t, n>; tf[f])

if t <> tprev ^ tprev <> 0; then

Emit(term t; postings P)

P:Reset()

P:Add(<n, f>)

tprev = t

method Close

other modifications
Other modifications
  • Partitionerand shuffle have to deliver all related <key, value> to same reducer
  • Custom partitioner so that all terms t go to the same reducer.
  • Lets go through a numerical example
what about retrieval
What about retrieval?
  • While MR is great for indexing, it is not great for retrieval.
index compression for space
Index compression for space
  • Section 4.5
  • (5,2), (7,3), (12,1), (49,1), (51,2)…
  • (5,2), (2,3), (5,1), (37,1), (2,2)…
miscellaneous stuff
Miscellaneous Stuff
  • How to MR Spam Filtering (Naïve Bayes solution) discussed in Ch.4 DDS? In training the model.
  • Write solution in the form of your main workflow configuration.
  • Prior is What is random probability of x occurring? Eg. What is the probability that the next person who walks into the class is a female?
nih solicitation in big data 2014
NIH Solicitation in Big Data (2014)
  • ..
  • This opportunity targets four topic areas of high need for researchers working with biomedical Big Data,

1. Data Compression/Reduction

2. Data Provenance

3. Data Visualization

4. Data Wrangling

odds ratio example from 4 16 2014 news article
Odds Ratio Example from 4/16/2014 news article
  • Woods is still favored to with the U.S. Open. He and Rory McIlroy are each 10/1 favorites on online betting site, Bovada. Adam Scott has the next best odds at 12/1…..
  • How to interpret this?
    • =
    • =
    • =
  • Woods is also the favorite to win the Open Championship at Hoylake in July. He\'s 7/1 there.

=

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