Caching query-biased snippets for efficient retrieval (Contributo in atti di convegno)

Type
Label
  • Caching query-biased snippets for efficient retrieval (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/1951365.1951379 (literal)
Alternative label
  • Ceccarelli D., Lucchese C. Orlando S., Perego R., Silvestri F. (2011)
    Caching query-biased snippets for efficient retrieval
    in 14th International Conference on Extending Database Technology, EDBT/ICDT '11, Uppsala, Sweden, March 21-24 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Ceccarelli D., Lucchese C. Orlando S., Perego R., Silvestri F. (literal)
Pagina inizio
  • 93 (literal)
Pagina fine
  • 104 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Progetto: Advanced Service Search and Enhancing Technological Solutions for the European Digital Library, ASSETS. - Grant agreement 250527 (EU). - ID Modulo Commessa: 2103 - ICT.P09.006.001 - 074 - Tecnologie avanzate, Sistemi e Servizi per Grid. - Area di valutazione 01 - Scienze matematiche e informatiche Ceccarelli, Diego ; Lucchese, Claudio ; Orlando, Salvatore; Perego, Raffaele ; Silvestri, Fabrizio (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://portal.acm.org/citation.cfm?id=1951365.1951379&coll=DL&dl=ACM&CFID=22890645&CF (literal)
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; Dipartimento di Informatica, Università di Pisa, Italy; Università di Venezia, Italy (literal)
Titolo
  • Caching query-biased snippets for efficient retrieval (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-0528-0 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Ailamaki A., Yahia S.A., Pate J., Risch T., Senellart P., Stoyanovich J. (literal)
Abstract
  • Web Search Engines' result pages contain references to the top-k documents relevant for the query submitted by a user. Each document is represented by a title, a snippet and a URL. Snippets, i.e. short sentences showing the portions of the document being relevant to the query, help users to select the most interesting results. The snippet generation process is very expensive, since it may require to access a number of documents for each issued query. We assert that caching, a popular technique used to enhance performance at various levels of any computing systems, can be very e ective in this context. We design and experiment several cache organizations, and we introduce the concept of supersnippet, that is the set of sentences in a document that are more likely to answer future queries. We show that supersnippets can be built by exploiting query logs, and that in our experiments a supersnippet cache answers up to 62% of the requests, remarkably outperforming other caching approaches. (literal)
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