Fulltext federated search of textbased digital libraries in peertopeer networks Information Retrieva

1
Full-text federated search of text-based digital
libraries in peer-to-peer networksInformation
Retrieval 2006, Springer
Paper presentation

• Jie Liu, Jamie Callan
• Language Technologies Institute, School of
  Computer Science, Carnegie Mellon University

Konstantinos Zaharis, Dept. of Comp.
Comm.Engineering, UTH
2
Paper Outline

• Introduction
• Overview / prior research
• Full-text federated search in p2p
• Test data
• Evaluation methodology experimental settings
• Results
• Conclusions and future work

3
Introduction

• Federated distributed
• Problem addressed use of p2p nets as a search
  layer for text-based digital libraries (dls).
• Why p2p? Because they do not need central
  authority (decentralized), they connect
  heterogeneous, multi-vendor and lightly managed
  enterprise nets. In short they are robust and
  scalable

4
Two types of environments in a p2p net

• Cooperative p2p environments each provider gives
  its own accurate resource description to each
  neighbouring directory service (hub)
• Uncooperative p2p environments each directory
  service conducts independently query-based
  sampling to obtain sample documents from its
  neighbouring providers in order to create their
  own resource description

5
Overview

• Distributed IR poses three main problems
• Resource representation discover content areas
  covered by each dl
• Resource selection decide which dls are most
  appropriate for an information need based on
  their descriptions
• Result merging merge ranked retrieval results
  from a set of selected dls

6
Source representation (prior research)

• STARTS cooperative protocol (Gravano et al.,
  Proc. of ACM SIGMOD, 1997)
• Query-based sampling for uncooperative
  environments (Callan, 2000). Directly refers to
  hidden web problem

7
Source selection (prior research)

• Algorithms based on resource ranking (CORI,
  gGIOSS, Kullback-Leibler divergence based)
• Threshold for resource selection usually set to a
  heuristic value (e.g. 5 or 10)

8
Result merging (prior research)

• 1st approch normalize resource specific document
  scores into resource independent document scores
  (CORI, SSL merging algorithms)
• 2nd approach recalculate document scores at
  directory service (Kirsch algorithm each
  resource provides summary statistics)

9
P2p network architecture

• Clients (information consumers) issue requests
  (queries)
• Servers (information providers, dls) route
  requests (query routing) to other servers
  (directory services) and respond to requests
  (retrieval)
• Lower level leaf nodes providers and consumers.
  Only connect to hubs
• Upper level hub nodes directory services.
  Connect with leaves and other hubs
• Query routing is the unique/critical issue in p2p
  nets

10
Structured vs hierachical p2p architecture

• Important distinction structured architecture
  uses DHT (distributed hash tables) which maps
  every data object to a distributed key. On the
  contrary hierarchical architectures that
  automatically discover contents of dl
  (appropriate for dynamic, heterogenous, privacy
  protected nets)
• Hierarchical architecture support sophisticated
  search techniques that are not constrainted to
  controlled or small vocabularies (more
  appropriate for full-text search). However they
  are more complex and demand higher communication
  costs
• Common characteristic construction of an overlay
  to organize peers for efficient query routing
  (semantic overlay networks)

11
Existing implementations

• PlanetP, each peer uses a TF.IDF algorithm to
  decide which peers to contact for information
  request (Cuenca-Acuna and Ngugen, 2002)
• pSearch, uses the semantic vector of each
  document (through LSI) to distribute document
  indices in a structured p2p net (Tang et al.,
  2003)

12
Paper contribution

• Revise and adapt methods to solve more
  efficiently the problems in hierarchical p2p nets
• Develop new approaches (e.g. resource ranking)
• Discriminate between cooperative and
  uncooperative environments
• Support thesis by extended experimental results

13
Resource description (1)

• Format a collection language model (lists of
  terms and frequencies along with corpus
  statistics)
• Resource can be a single provider (dl), a hub
  (multiple connected providers) or a neighborhood
  (all peers reachable from a hub)
• Description of providers cf slide 9
• Description of hubs aggregation of description
  of neighboring providers (within 1 hop)

14
Neighborhood description

• Routing indices termsfreqpath to other docs
  (Crespo and Garcia-Molina, 2002b)
• Each hub calculates and sends to its hub neighbor
  the resource description of its neighborhood
• Total of documents aggregated in exponential
  time
• Detection/avoidance of graph cycles because it
  affects the accuracy of descriptions

15
Resource selection (2)

• Query routing directing queries to peers that
  are most likely to contain relevant documents.
  Cost proportional to of messages carrying the
  query
• Flooding technique accurate but inefficient
  (exponential of query messages)
• Random forward technique relatively efficient
  but inaccurate
• Then what?

16
Resource ranking (full-text)

• Providers use of K-L divergence resource ranking
  algorithm to calculate P(Pi Q) (Si and Callan,
  2004)
• Hubs same as above with aggregation over
  selected neighborhoods
• After ranking, the idea is to select the
  top-ranked entities by either a) specifying a
  predetermined number (not as good for dynamically
  changed nets) or b) letting the entities to learn
  their own threshold values automatically and
  autonomously

17
Unsupervised threshold learning method

• Providers estimate ranking scores of relevant and
  non-relevant documents using the merged retrieval
  results of a set of training queries (set-based
  threshold learning)
• Hubs, however use individual training queries for
  each member of their neighborhoods
  (individual-based threshold learning

18
Result merging (3)

• Cooperative environments use of Kirsch algorithm
  (Kirsch, 1997) modified to the point that it no
  longer needs global statistics (fewer costs)
• Uncooperative environments no summary statistics
  are available, so adapt the Semi-Supervised-Learni
  ng algorithm (Si and Callan, 2003a). Use linear
  regression with local weights and overlapping
  documents

19
a real P2P network search model
SourceFull text federated search in P2P
networks, Lu J., PhD Dissertation, CMU 2007
20
Test data and evaluations

• Use of WT10g-based testbed collection
• provides (websites) 2500
• hubs (content-based clustering) 25
• documents 1500000
• Queries automatically generated (by extracting
  key terms from documents)
• Evaluation criteria a) search accuracy and b)
  query routing efficiency

21
Experimental settings

• Four methods for resource selection
• Flooding
• Random selection
• Full-text selection using a fixed threshold (e.g.
  1 of the top-ranked neighbouring hubs)
• Full-text selection using learned thresholds
• TTL (time-to-live) value for each query message
  set to 6
• Query-based sampling for resource representation
  in uncooperative environments
• of training points to apply SSL method set to 3

22
Experimental results (numbers)
23
Experimental results show

• Full-text selection performs better than flooding
  or random selection
• Using learned thresholds for resource selection
  yields a few more query messages (than using a
  fixed threshold) but improves search accuracy
• Uncooperative environments exhibit 10 search
  performance degradation in comparision to
  cooperative once, which is generally accepted

24
Conclusions and future work

• Enhance hub functionality so as not only to
  provide sufficient information for its connected
  providers, but also calculate a path to other,
  probably useful, peers (provider routing
  technique)
• Method works well for small/medium sized p2p nets
  with regulated network structures and organized
  content distribution. But what happens in
  larger-scale networks?
• What happens in dynamically/temporally evolved
  nets? What about load balancing, dynamic
  clustering and fault tolerance?

25

• Comments
• Paper contained no intriguing ideas but proposed
  practical modifications to existing methods
• Writing style demonstrated frequent repetitions,
  verbatimism and often vagueness
• It is obvious that researchers are more inclined
  to better empirical results/tools for real world
  applications than theoretical models
• All references are taken from commenting paper
  reference list

26
Thank you for your attention!

• Any questions?