The Effect of Collection Organization and Query Locality on IR Performance

2003/07/28

Park, Dae-Won(bluepepe@pusan.ac.kr)

Contents

Introduction of Distributed IR Related Works System Architecture Query Locality Experiments Conclusions

Introduction(1)

Distributed IR System 연구 목적

Content 증가에 따른 검색 성능의 유지 향상 Decrease query response time, maintain effectiven

ess 관련 연구

Caching ( Martin and Russell,1991; Markatos,1999) Collection selection ( Voorhees,1995; Callan,1995; Frenc

h,1999; Xu and Croft, 1999) Partial replication (Lu and Mackinley, 1999)

Introduction(2)

In this paper Use previous works

Collection selection, partial replication Use collection organization

Determine when and how to use collection selection and replication

Classify collection organizations as either by topic, source, or random

Related Works

Architecture IR versus database systems Unstructured data versus structured data

IR versus the web Static collection : case law, journal articles,,,

Caching Collection selection

Related Works : Architecture(1)

architecture for parallel and distributed IRHarman et al.,1991

Show the feasibility of a distributed IR system by developing a prototype architecture

Burkowski,1990, Burkowski et al., 1995 Simulation study which measures the retrieval performance o

f a distributed IR system Two strategies for distributing a fixed workload

Equally distribute the text collection Split servers into query evaluation group and document retrieval

Related Works : Architecture(2)

Couvreur et al.,1994 Analyze the performance and cost factors Three different hardware architectures

Hawking,1997 Design and implement a parallel IR system, PADRE

97, on a collection of workstations Central process : check user command, broadcast to the

IR engines and merge results

Related Works : Architecture(3)

Cahoon and Mckinley,1996 & Cahoon,1999 Distributed IR system based on INQUERY Collection

Uniformly distributed Up to 128GB using a variety of workloads

Measure performance as a function of system parameters such as client command rate, number of document collections, , ,

Related Works : Caching

Markatos, 1999 Caches web queries and their results

Require exact match ( 단점 ) Increase locality by determining query similarity to replica

s

Related Works : Collection selection(1)

Working on how to select the most relevant collection for a given query Danzig et al., 1991

Use a hierarchy of brokers to maintain indices Support Boolean keyword matching

Voorhees et al., 1995 Exploit similarity between a new query and relevance judg

ments for previous queries

Related Works : Collection selection(2)

Callan et al., 1995 Adapt the document inference network to ranking collecti

ons by replacing the document node with the collection node

Store the collection ranking inference network with document frequencies and term frequencies

Xu an Croft, 1999 Propose cluster-based language model for collection sele

ction Apply clustering algorithms to organize document into coll

ections based on topics, and then apply the approach of Callan et al.,1995 to select the most relevant collections

System Architecture(1)

Architecture for a distributed information retrieval system base on INQUERY

Client 1

Client 2

Client 3

Client m

Connection Broker

INQUERY Server 1

INQUERY Server 2

INQUERY Server 3

Collections

INQUERY Server n

System Architecture(2)

use collection selector

Client 1

Client 2

Client 3

Client m

Connection Broker

Collection Selector

INQUERY Server 1

INQUERY Server 2

INQUERY Server 3

Collections

INQUERY Server n

System Architecture(3)

replica selector and collection selector

Client 1

Client 2

Client 3

Client m

Connection Broker

Replica Selector Collection

Selector

INQUERY Server 1

INQUERY Server k

Original

Collections

INQUERY Server K+1

INQUERY Server pReplica 1

INQUERY Server n Replica q

System Architecture(4)

Collection Set of documents No overlaps between documents in any two

collections Organized either by topic, source(for

example, newspaper, journals,,, ), or randomly

Connection Brokers A process that keeps track of all registered

clients and INQUERY servers

System Architecture(5)

Connection Brokers A process that keeps track of all registered

clients and INQUERY servers Forward command to the appropriate servers Maintain intermediate result

Merge result with other results Send the final result to the client

System Architecture(6)

Collection selector Choose the most relevant collections from

some set of collections on a query-by-query basis

Maintain a collection selection database with collection level information for each collection

System Architecture(7)

Replica selector Replicate a portion of the original collection

(if the same or related queries repeat) Build a partial replica for the whole

Subset of the original collection

Collection Organization

Collection access skew When queries are relevant to a few collections and

collection selection concentrates queries in these collections

Model using a Zipf-like function Z(i) = c/i1-, where c=1/ (1/j1-), 1 <= i <= C

Query Locality

If users repeatedly issue queries on the same topics, a set of document will receive more hits, which results in query locality Partial replication off-load the services on

original collections Correlation with collection access skew

If query locality is low, collections accessed uniformly

If query locality is high, collection access may range from uniform to highly skewed

Experiments(1)

Demonstrate the performance impact of collection organization and query locality256 GB of data using 9 servers 8 servers : store the original collections 9th server : store 32 GB partial replica or

partition the data further

Include collection selector and replica selector in the connection broker

Experiments(2)

Random Organization(1) Randomly partition data over collections

Experiments(3)

Random Organization(2) Randomly partition data over collections

Experiments(4)

Source Organization Collections are organized by source

Experiments(5)

Source Organization Collections are organized by source

Experiments(6)

Topic organization Collections are organized by source

Experiments(7)

Topic organization Collections are organized by source

Conclusions

Effect of query locality and collection organization on the design and performance of IR system Collection selection improves performance

significantly when either collection access is fairly uniform or collections are organized based on topics

Query locality enables partial replication to improve performance over collection selection with partitioning