http://www.cnr.it/ontology/cnr/individuo/prodotto/ID277741
Query processing in highly-loaded search engines (Contributo in atti di convegno)
- Type
- Label
- Query processing in highly-loaded search engines (Contributo in atti di convegno) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/978-3-319-02432-5_9 (literal)
- Alternative label
Broccolo D., Macdonald C., Orlando S., Ounis I., Perego R., Silvestri F., Tonellotto N. (2013)
Query processing in highly-loaded search engines
in SPIRE'13 - String Processing and Information Retrieval. 20th International Symposium, Jerusalem, 7-9 October 2013
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Broccolo D., Macdonald C., Orlando S., Ounis I., Perego R., Silvestri F., Tonellotto N. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://link.springer.com/chapter/10.1007%2F978-3-319-02432-5_9 (literal)
- Note
- ISI Web of Science (WOS) (literal)
- Scopu (literal)
- PuMa (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- CNR-ISTI, Pisa, Italy; University of Glasgow, Glasgow, United Kingdom; CNR-ISTI, Pisa, Italy; University of Glasgow, Glasgow, United Kingdom; CNR-ISTI, Pisa,Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy. (literal)
- Titolo
- Query processing in highly-loaded search engines (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-3-319-02431-8 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Oren Kurland, Moshe Lewenstein, Ely Porat (literal)
- Abstract
- While Web search engines are built to cope with a large number of queries, query traffic can exceed the maximum query rate supported by the underlying computing infrastructure. We study how response times and results vary when, in presence of high loads, some queries are either interrupted after a fixed time threshold elapses or dropped completely. Moreover, we introduce a novel dropping strategy, based on machine learned performance predictors to select the queries to drop in order to sustain the largest possible query rate with a relative degradation in effectiveness. (literal)
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