Running Time Prediction for Web Search Queries

Rojas, Oscar Andres Cuellar; Gil-Costa, Verónica

doi:10.1007/978-3-319-32152-3_20

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A preliminary version of our work was presented in [11] where we described the application of the DFT technique to predict the running time of the BM-WAND algorithm. This provided evidence that our DFT based strategy was able to achieve promising results.…”

Section: Contributionmentioning

confidence: 99%

A DFT-Based Running Time Prediction Algorithm for Web Queries

Rojas

Gil-Costa

2021

Future Internet

View full text Add to dashboard Cite

Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation.

show abstract

Section: Contributionmentioning

confidence: 99%