Query-Driven Indexing for Scalable P2P Text Retrieval

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Query-Driven Indexing for Scalable P2P Text
Retrieval
Infoscale07, June 6-8, 2007 Suzhou, China

• Gleb Skobeltsyn
• EPFL, Switzerland
• June 6, 2007

• Joint work with
• Toan Luu
• Ivana Podnar Žarko
• Martin Rajman
• Karl Aberer

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Goal

• Our goal is to achieve scalable full-text
  retrieval with structured P2P networks (DHTs)

• Each peer
• Provides resources (bandwidth, storage)
• Searches the whole network
• Publishes its own documents

DHT
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Naïve (single-term) approach

• ... is to distribute the global inverted index in
  a DHT

This slide was borrowed from B. T. Loo, J. M.
Hellerstein, R. Huebsch, S. Shenker, I. Stoica
presentation Enhancing P2P File-Sharing with an
Internet-Scale Query Processor
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Indexing with Highly Discriminative Keys
1 Scalable Peer-to-Peer Web Retrieval with
Highly Discriminative Keys I. Podnar, M. Rajman,
T. Luu, F. Klemm, K. Aberer in ICDE07,
Istambul, Turkey
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Indexing with HDKs main properties

• Distributed index contains key,PL pairs
• Each key corresponds to a term or a set of terms
• Each key is assigned to a posting list
• Each posting list stores at most DFmax top-ranked
  document references.
• Data-Driven key generation
• Each time a new document is indexed, some posting
  lists for a key k can reach the max size of DFmax
  
• It triggers the generation of new keys (k
  other frequent keys)
• Proximity Filter a document qualifies for a key
  t1t2 if t1 is close to t2 (specified by a window
  size w).

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HDK exhaustive data driven indexing

• Pros
• ICDE07 paper proves that the number of keys
  grows linearly
• Elegant key generation mechanism
• Low bandwidth while query processing (PLs of
  limited size)
• Cons
• Practically the number of keys is LARGE 68M for
  0.6M docs
• High bandwidth consumption at indexing
• Problem
• Too many keys are superfluous (almost never used)

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Query Driven Indexing

• Lets index only what is queried!

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Contents

• Introduction
• HDK approach for indexing
• Query-driven approach for indexing/retrieval
• Indexing structure
• Example
• ONM
• Scalability
• Evaluation
• Conclusion

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Query-Driven Index (QDI)

• Query-Driven Indexing strategy solves
  the Too-Many-Keys problem
• Avoids maintenance of superfluous keys
• Generates only such keys that are requested by
  users
• Utilizes query-log to discover such keys
• Problems
• Indexing of a new key requires a
  bandwidth-efficient mechanism to obtain the top-k
  posting list associated with the key
• Opportunistic Notification Mechanism
• (smart-broadcast)
• Incomplete index causes degradation of query
  results quality
• Show that the degradation is low

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Which keys to index?

• Each single-term found in the document collection
  is has to be indexed.
• We call all single-term keys a basic single term
  index.
• The posting lists are truncated at DFmax.
• A key k is non-superfluous and can be activated
  iff
• k is popular QF(k) QFmin, where QF(k) is the
  popularity of the key k derived from the
  available query log and QFmin is a parameter for
  our model (popularity filter).
• k contains from 2 to smax terms 2k smax,
  where smax is a parameter of our model (size
  filter).
• all immediate sub-keys of k (of size k-1) are
  indexed and their associated postings lists are
  truncated (redundancy filter).

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QDI Retrieval

• Single term index is generated
• Process abc
• Probe Pabc
• Probe Pab Pbc and Pac
• Probe Pa Pb and Pc
• Obtain top-DFmax results for a, b and c (ranked
  w.r.t a, b and c respectively)
• Contact peers in the list, re-rank the obtained
  results w.r.t abc
• Output top-10
• Inc. the QF for ab, bc and ac
• Activate (index) ac

popular
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QDI Retrieval 2

• Assume the frequency of b is below DFmax
• Note, how the redundancy filter would simplify
  the lattice in such a case
• (grayed nodes cannot be activated)

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QDI Retrieval 3

• Single term index is generated and ac is indexed
• Process abc
• Probe Pabc
• Probe Pab Pbc and Pac obtain the result for ac
• Probe Pb and obtain the result for b
• Contact all peers in the list to re-rank the
  obtained results w.r.t abc
• Output top-10
• Inc. the QF for ab, bc and ac

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Opportunistic Notification Mechanism

• ONM used to activate a new multi-term key
• ONM is a smart broadcast with the following
  features
• It is based on the shower multicast 2 each
  peer within a specified range is contacted only
  once
• Notifications are small and low-priority gt
  piggybacking
• Broadcast is split into several multicast
  sessions, each time pruning low-score documents
• It uses the high-performance DHT layer 3
• 2 A. Datta, M. Hauswirth, R. Schmidt, R. John,
  K. Aberer
• Range Queries in Tree-Structured Overlays, in
  P2P05
• 3 F. Klemm, J.-Y. Le Boudec, D. Kostic, K.
  Aberer
• Improving the Throughput of Distributed Hash
  Tables Using Congestion-Aware Routing, in
  IPTPS'07

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Scalability

• The retrieval traffic is bounded by a constant
  due to trun-cated posting lists (depends on DFmax
  and a query size)
• The indexing traffic depends on the number of
  keys to be activated.
• The number of keys in the HDK approach (UPPER
  BOUND) is proven to grow linearly with the number
  of peers, if each peer provides a limited number
  of documents
• The number of keys does not depend on the
  document collection size but only on the size of
  the query log
• We can use the QFmin parameter to adjust the
  tradeoff indexing traffic lt-gt retrieval
  quality

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Contents

• Introduction
• HDK approach for indexing
• Query-driven approach for indexing/retrieval
• Indexing structure
• Example
• ONM
• Scalability
• Evaluation
• Conclusion

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Overlap experiment

• Use the Wikipedia query-log (9M
  queries/9-10.2004) to build the index
• Choose randomly 3K test queries
• Answer each test query with Google and compare to
  the union of top-DFmax Google results for each of
  its combinations that are indexed according to
  the logs.
• Mimics our P2PIR system if Googles ranking is
  used.
• Example

Non-superfluous (indexed) combinations
Original query
X
X
overlap_at_53/560
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Overlap example

• Cut-n-paste from the simulation log

gtid481, qwhat did babe ruth do in the 1920
1920 babe ruth, qf0 ----gt Ov_at_100 100
1920 babe, qf0 ---------gt Ov_at_100 9
1920 ruth, qf1 ---------gt Ov_at_100 33
babe ruth, qf495 -------gt Ov_at_100 69
---1920, qf716 ------------gt Ov_at_100 1
---babe, qf3196 -----------gt Ov_at_100 2
---ruth, qf1653 -----------gt Ov_at_100 7
Size 192, Keys used 2, Overlap_at_100 94
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Overlap with Google
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Overlap with Yahoo
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Overlap with Google (no/partial/full overlap)
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P2P Index Simulations

• Number of keys depends only on the query log size
  and QFmin!
• Does not depend on the collection size!
• Number of keys is much smaller than for the HDK
  approach 68M keys for 650K doc

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Real query logs?

• Wikipedia queries are unrealistic (too skewed) as
  users know what they want.
• Real web-queries might
• perform worse?
• Large scale experiments
• with real web queries and
• the TREC collection in 4
• 4 Web Text Retrieval with a P2P Query-Driven
  Index G. Skobeltsyn, T. Luu, I. Podnar
  Žarko, M. Rajman, K. Aberer To appear in
  SIGIR07

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Conclusions

• We presented the query-driven indexing strategy
  for scalable web text retrieval with structured
  P2P networks
• Stores posting lists in a DHT for terms and term
  combinations
• Stores at most DFmax top document references in a
  posting list
• Efficiently collects the query statistics in a
  distributed fashion
• Based on this statistics activates (indexes) only
  popular keys
• Computes the result of a multi-term query based
  only on the index entries available at the
  moment no costly intersections
• We also showed that
• With real query-logs our approach achieves good
  retrieval quality
• The QFmin parameter adjusts the traffic/quality
  tradeoff

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Last slide

• Thank you for your attention!
• Questions?