Abstract The proliferation of online information resources increases the importance of effective and efficient distributed searching. Distributed searching is cast in three parts – database selection, query processing, and results merging. In this paper we examine the effect of database selection on retrieval performance. We look at retrieval performance in three different distributed retrieval testbeds and distill some general results. First we find that good database selection can result in better retrieval effectiveness than can be achieved in a centralized database. Second we find that good performance can be achieved when only a few sites are selected and that the performance generally increases as more sites are selected. Finally we find that when database selection is employed, it is not necessary to maintain collection wide information (CWI), e.g. global idf. Local information can be used to achieve superior performance. This means that distributed systems can be engineered with more autonomy and less cooperation. This work suggests that improvements in database selection can lead to broader improvements in retrieval performance, even in centralized (i.e. single database) systems. Given a centralized database and a good selection mechanism, retrieval performance can be improved by decomposing that database conceptually and employing a selection step. 1

We compare the performance of two database selection algorithms reported in the literature.Their performance is compared using a common testbed designed specifically for database selection techniques.The testbed is a decomposition of the TREC/TIPSTER data into 236 subcollections.The databases from our testbed were ranked using both the gGlOSS and CORI techniques and compared to a baseline derived from TREC relevance judgements.We examined the degree to which CORI and gGlOSS approximate this baseline.Our results con rm our earlier observation that the gGlOSS Ideal(l) ranks do not estimate relevancebased ranks well.We also nd that CORI is a uniformly better estimator of relevance-based ranks than gGlOSS for the test environment used in this study.Part of the advantage of the CORI algorithm can be explained by a strong correlation between gGlOSS and a size-based baseline (SBR).We also nd that CORI produces consistently accurate rankings on testbeds ranging from 100{921 sites.However for a given level of recall, search eort appears to scale linearly with the number of databases.

We describe a testbed for database selection techniques and an experiment conducted using this testbed.The testbed is a decomposition of the TREC/TIPSTER data that allows analysis of the data along multiple dimensions, including collection-based and temporal-based analysis.We characterize the subcollections in this testbed in terms of number of documents, queries against which the document,s have been evaluated for relevance, and distribution of relevant documents.We then present initial results from a study conducted using this testbed that examines the effectiveness of the gGlOSS approach to database selection.The databases from our testbed were ranked using the gGl0S.S techniques and compared to the gGlOSS I&l(l) baseline and a baseline derived from TREC relevance judgements.We have examined the degree to which several gGlOSS estimate functions approximate these baselines.Our initial results confirm that the gGZOSS estimators are excellent predictors of the Ideal(Z) ranks but that the Ideal(l) ranks do not estimate relevance-based ranks well.

Using the vector space information retrieval model, we show that the update of term weights under document insertions is computationally expensive for weighting schemes that use collection statistics and normalization by document vector lengths. In the dynamic setting, we argue that strict adherence to such schemes is impractical and unnecessary as long as retrieval effectiveness commensurate with strict adherence is attained. Experiments using standard test collections as a source of document insertions support this argument. These experiments indicate that term weights may drift from their mathematically defined values without a serious loss of retrieval effectiveness. The only problematic setting is when new terms are present in newly inserted documents. Ignoring these terms can cause an effectiveness degradation. 1 Introduction The rapid growth in online information has fueled recent interest in techniques to handle the burgeoning flood of data becoming electronically available. ...

We find that dissemination of collection wide information (CWI) in a distributed collection of documents is needed to achieve retrieval effectiveness comparable to a centralized collection.Complete dissemination is unnecessary.The required dissemination level depends upon how documents are allocated among sites.Low dissemination is needed for random document allocation, but higher levels are needed when documents are allocated based on content.We define parameters to control dissemination and document allocation and present results from four test collections.We define the notion of iso-knowledge lines with respect to the number of sites and level of dissemination in the distributed archive, and show empirically that iso-knowledge lines are also zsoefiectivene.wlines when documents are randomly allocated.