Scalable collection summarization and selection

Dolin, Ron; Agrawal, Divyakant; Abbadi, E. El

doi:10.1145/313238.313257

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…Methods relying on manual classification such as used by CiteLine Professional and InvisibleWeb would be impractical due to the lack of such classification over WT2g documents. Due to time constraints even methods based on automatic classification such as Pharos [5] are not evaluated here.…”

Section: Server Selection Methodsmentioning

confidence: 99%

Server selection on the World Wide Web

Craswell

Bailey

Hawking

2000

Proceedings of the Fifth ACM Conference on Digital Libraries

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We evaluate server selection methods in a Web environment, modeling a digital library which makes use of existing Web search servers rather than building its own index. The evaluation framework portrays the Web realistically in several ways. Its search servers index real Web documents, are of various sizes, cover different topic areas and employ different retrieval methods. Selection is based on statistics extracted from the results of probe queries submitted to each server. We evaluate published selection methods and a new method for enhancing selection based on expected search server effectiveness.Results show CORI to be the most effective of three published selection methods. CORI selection steadily degrades with fewer probe queries, causing a drop in early precision of as much as 0 05 (one relevant document out of 20). Modifying CORI selection based on an estimation of expected effectiveness disappointingly yields no significant improvement in effectiveness. However, modifying CORI based on known effectiveness does yield small but significant improvements. Other results are that a very effective selection of ten servers outperforms both a selection of all servers and a centralised index covering all documents. Finally, acknowledging coverage limitations of real Web indexes we model centralised indexes of half and a quarter of the document servers, resulting in a sharp drop in effectiveness.

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Section: Server Selection Methodsmentioning

confidence: 99%

Server selection on the World Wide Web

Craswell

Bailey

Hawking

2000

Proceedings of the Fifth ACM Conference on Digital Libraries

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“…where T = (p(w|D)·|D|)/(p(w|D)·|D|+50+150· cw(D) mcw ), 8 We adapted the technique in [13] slightly so that each database is classified under exactly one category. 9 Experiments using shrinkage together with ReDDE [27], a promising, recently proposed database selection algorithm, remain as interesting future work.…”

Section: Database Selection Algorithmsmentioning

confidence: 99%

When one sample is not enough

Ipeirotis

Gravano

2004

Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data

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Database selection is an important step when searching over large numbers of distributed text databases. The database selection task relies on statistical summaries of the database contents, which are not typically exported by databases. Previous research has developed algorithms for constructing an approximate content summary of a text database from a small document sample extracted via querying. Unfortunately, Zipf's law practically guarantees that content summaries built this way for any relatively large database will fail to cover many low-frequency words. Incomplete content summaries might negatively affect the database selection process, especially for short queries with infrequent words. To improve the coverage of approximate content summaries, we build on the observation that topically similar databases tend to have related vocabularies. Therefore, the approximate content summaries of topically related databases can complement each other and increase their coverage. Specifically, we exploit a (given or derived) hierarchical categorization of the databases and adapt the notion of "shrinkage" -a form of smoothing that has been used successfully for document classification-to the content summary construction task. A thorough evaluation over 315 real web databases as well as over TREC data suggests that the shrinkage-based content summaries are substantially more complete than their "unshrunk" counterparts. We also describe how to modify existing database selection algorithms to adaptively decide -at run-time-whether to apply shrinkage for a query. Our experiments, which rely on TREC data sets, queries, and the associated "relevance judgments," show that our shrinkage-based approach significantly improves state-of-the-art database selection algorithms, and also outperforms a recently proposed hierarchical strategy that exploits database classification as well.

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“…However, there are approaches that incorporate additional information; for example, considering both textual and nontextual information Dolin et al [1997Dolin et al [ , 1998Dolin et al [ , 1999 or set different goals, for example, efficiency [Moffat and Zobel 1995], minimizing the cost of retrieving documents [Fuhr 1999] or accessing collections [Hawking and Thistlewaite 1999]. Craswell et al [2000] argued that the retrieval performance at a collection should be incorporated into the collection selection step.…”

Section: Additional Considerationsmentioning

confidence: 99%

Comparing the performance of collection selection algorithms

Powell

French

2003

ACM Trans. Inf. Syst.

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The proliferation of online information resources increases the importance of effective and efficient information retrieval in a multicollection environment. Multicollection searching is cast in three parts: collection selection (also referred to as database selection), query processing and results merging. In this work, we focus our attention on the evaluation of the first step, collection selection.In this article, we present a detailed discussion of the methodology that we used to evaluate and compare collection selection approaches, covering both test environments and evaluation measures. We compare the CORI, CVV and gGlOSS collection selection approaches using six test environments utilizing three document testbeds. We note similar trends in performance among the collection selection approaches, but the CORI approach consistently outperforms the other approaches, suggesting that effective collection selection can be achieved using limited information about each collection.The contributions of this work are both the assembled evaluation methodology as well as the application of that methodology to compare collection selection approaches in a standardized environment.

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Scalable collection summarization and selection

Cited by 17 publications

References 15 publications

Server selection on the World Wide Web

Server selection on the World Wide Web

When one sample is not enough

Comparing the performance of collection selection algorithms

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