Elasticsearch: The Definitive Guide

By C. Gormley, Zachary J. Tong. 2015

224 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reading Wikipedia to Answer Open-Domain Questions (2017, methodology): “A simple inverted index lookup followed by term vector model scoring performs quite well on this task for many question types, compared to the built-in ElasticSearch based Wikipedia Search API (Gormley and Tong, 2015).”
Trick Me If You Can: Human-in-the-Loop Generation of Adversarial Examples for Question Answering (2018, methodology): “For the IR model, we use the ElasticSearch Highlight API (Gormley and Tong, 2015), which provides word importance scores based on query matches from the inverted index.”
An anatomy for neural search engines (2019, methodology): “A popular one is the square rooted value of the raw count, and we explicit this version because it is the one used by Elasticsearch [Gormley and Tong, 2015], the implementation we use throughout this work:”
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions (2018, background): “In particular, the Elasticsearch Highlight API (Gormley and Tong, 2015) visually highlights words in the Evidence section of the interface.”
Serverless Computation with OpenLambda (2016, background): “For example, with search, one may want to scan many inverted indexes in parallel and then gather and aggregate the results [26].”