Adaptive query-based sampling of distributed collections

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4209 LNCS, Issue , 2006, Pages 316-326

  Baillie, Mark ^a   Azzopardi, Leif ^a   Crestani, Fabio ^a  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DATA ACQUISITION; DISTRIBUTED COMPUTER SYSTEMS; HEURISTIC METHODS; QUERY LANGUAGES; SAMPLING;

DISTRIBUTED INFORMATION RETRIEVAL; PREDICTIVE LIKELIHOOD; QUERY BASED SAMPLING;

INFORMATION RETRIEVAL SYSTEMS;

EID: 33750362813     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11880561_26     Document Type: Conference Paper

References (16)

1. L. Azzopardi, M. Girolami, and C. J. Risjbergen. Investigating the relationship between language model perplexity and IR precision-recall measures. In Proceedings of the 26th ACM SIGIR conference, pages 369-370, 2003.
2. R. A. Baeza-Yates. Applications of web query mining. In Proceedings of the 27th ECIR, pages 7-22, Santiago de Compostela, Spain, 2005.
3. M. Baillie, L. Azzopardi, and F. Crestani. Towards better measures: Evaluation of estimated resource description quality for distributed IR. In First International Conference on Scalable Information Systems. IEEE CS Society, 2006.
4. N. J. Belkin and W. B. Croft. Information filtering and information retrieval: two sides of the same coin. Communications of the ACM, 35(12):29-38, 1992.
5. C. Buckley and E. M. Voorhees. Evaluating evaluation measure stability. In Proceedings of the 23rd ACM SIGIR conference, pages 33-40, 2000.
6. J. P. Callan. Advances in information retrieval, chapter Distributed information retrieval, pages 127-150. Kluwer Academic Publishers, 2000.
7. J. P. Callan and M. Connell. Query-based sampling of text databases. ACM Transactions of Information Systems, 19(2):97-130, 2001.
8. M. H. Degroot. Optimal Statistical Decisions (Wiley Classics Library). Wiley-Interscience, April 2004.
9. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley-Interscience Publication, 2000.
10. T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1-2):177-196, 2001.
11. P. G. Ipeirotis and L. Gravano. When one sample is not enough: improving text database selection using shrinkage. In Proceedings of the ACM SIGMOD Conference, pages 767-778, 2004.
12. S. Kullback. Information theoery and statistics. Wiley, New York, 1959.
13. M. Shokouhi, F. Scholer, and J. Zobel. Sample sizes for query probing in uncooperative distributed information retrieval. In APWeb 2006, volume 3841, pages 63-75. Springer Lecture Notes in Computer Science, 2006.
14. L. Si and J. P. Callan. Modeling search engine effectiveness for federated search. In Proceedings of the 28th ACM SIGIR Conference, pages 83-90, 2005.
15. J. Xu and W. B. Croft. Cluster-based language models for distributed retrieval. In Proceedings of the 22nd ACM SIGIR conference, pages 254-261, 1999.
16. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Transaction of Information Systems, 22(2):179-214, 2004.