A study of the impact of global statistics in distributed information retrieval
type: CreativeWork
description: Today’s information retrieval systems have to deal with very large data collections and take a distributed approach to achieve scalable retrieval performance. The most widely used approach, called document-partitioning, is to partition the data among multiple search-nodes, which then index their sub-collection independently and are responsible for scoring documents present in their index, against queries. Most of the famous document scoring functions depend on various global (collection-wide) statistics such as document frequency of terms. However, as search-nodes don’t have access to global-statistics and rely on local (sub-collection-wide) statistics for the purpose of scoring, document-partitioning can result in a degraded retrieval performance. In this thesis, we study the impact of the lack of global-statistics on the retrieval performance of a distributed information retrieval (DIR) system. Our experiments show that the performance, as indicated by multiple measures, degrades as the number of search-nodes are increased. We thus conclude that global-statistics are essential to the retrieval performance in a distributed setup. Finally, we present a novel scheme for lazy and adaptive dissemination of global-statistics in a document-partitioned DIR system.
abstract: Today’s information retrieval systems have to deal with very large data collections and take a distributed approach to achieve scalable retrieval performance. The most widely used approach, called document-partitioning, is to partition the data among multiple search-nodes, which then index their sub-collection independently and are responsible for scoring documents present in their index, against queries. Most of the famous document scoring functions depend on various global (collection-wide) statistics such as document frequency of terms. However, as search-nodes don’t have access to global-statistics and rely on local (sub-collection-wide) statistics for the purpose of scoring, document-partitioning can result in a degraded retrieval performance. In this thesis, we study the impact of the lack of global-statistics on the retrieval performance of a distributed information retrieval (DIR) system. Our experiments show that the performance, as indicated by multiple measures, degrades as the number of search-nodes are increased. We thus conclude that global-statistics are essential to the retrieval performance in a distributed setup. Finally, we present a novel scheme for lazy and adaptive dissemination of global-statistics in a document-partitioned DIR system.
author: Sehrawat, Nipun
language: en
keywords: Distributed Information Retrieval, Global Statistics, Retrieval Performance
date published: 2012-06-27T21:19:22Z
date modified: 2026-07-22T22:25:30Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/31920