Dynamic pruning strategies are effective yet permit efficient retrieval by pruning - i.e. not fully scoring all postings of all documents matching a given query. However, the amount of pruning possible for a query can vary, resulting in queries with similar properties (query length, total numbers of postings) taking different amounts of time to retrieve search results. In this work, we investigate the causes for inefficient queries, identifying reasons such as the balance between informativeness of query terms, and the distribution of retrieval scores within the posting lists. Moreover, we note the advantages in being able to predict the efficiency of a query, and propose various query efficiency predictors. Using 10,000 queries and the TREC ClueWeb09 category B corpus for evaluation, we find that combining predictors using regression can accurately predict query response time. © 2011 ACM.
Query efficiency prediction for dynamic pruning
Tonellotto N.;
2011-01-01
Abstract
Dynamic pruning strategies are effective yet permit efficient retrieval by pruning - i.e. not fully scoring all postings of all documents matching a given query. However, the amount of pruning possible for a query can vary, resulting in queries with similar properties (query length, total numbers of postings) taking different amounts of time to retrieve search results. In this work, we investigate the causes for inefficient queries, identifying reasons such as the balance between informativeness of query terms, and the distribution of retrieval scores within the posting lists. Moreover, we note the advantages in being able to predict the efficiency of a query, and propose various query efficiency predictors. Using 10,000 queries and the TREC ClueWeb09 category B corpus for evaluation, we find that combining predictors using regression can accurately predict query response time. © 2011 ACM.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
