Learning to predict response times for online query scheduling (Contributo in atti di convegno)

Type
Label
  • Learning to predict response times for online query scheduling (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2348283.2348367 (literal)
Alternative label
  • Macdonald C., Tonellotto N., Ounis I. (2012)
    Learning to predict response times for online query scheduling
    in 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, Portland, OR, USA, 12-16 August 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Macdonald C., Tonellotto N., Ounis I. (literal)
Pagina inizio
  • 621 (literal)
Pagina fine
  • 630 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?id=2348367&CFID=179990712&CFTOKEN=56023105 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (literal)
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • University of Glasgow, Glasgow, United Kingdom; CNR-ISTI, Pisa, Italy; University of Glasgow, Glasgow, United Kingdom (literal)
Titolo
  • Learning to predict response times for online query scheduling (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-1472-5 (literal)
Abstract
  • Dynamic pruning strategies permit efficient retrieval by not fully scoring all postings of the documents matching a query - without degrading the retrieval effectiveness of the topranked results. However, the amount of pruning achievable for a query can vary, resulting in queries taking different amounts of time to execute. Knowing in advance the execution time of queries would permit the exploitation of online algorithms to schedule queries across replicated servers in order to minimise the average query waiting and completion times. In this work, we investigate the impact of dynamic pruning strategies on query response times, and propose a framework for predicting the efficiency of a query. Within this framework, we analyse the accuracy of several query efficiency predictors across 10,000 queries submitted to in-memory inverted indices of a 50-million-document Web crawl. Our results show that combining multiple efficiency predictors with regression can accurately predict the response time of a query before it is executed. Moreover, using the efficiency predictors to facilitate online scheduling algorithms can result in a 22% reduction in the mean waiting time experienced by queries before execution, and a 7% reduction in the mean completion time experienced by users. (literal)
Editore
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Prodotto
Autore CNR di
Editore di
Insieme di parole chiave di
data.CNR.it