Affiliation:
1. ISTI-CNR, Pisa, Italy
2. University of Pisa, Pisa, Italy
Abstract
The data structure at the core of large-scale search engines is the
inverted index
, which is essentially a collection of sorted integer sequences called
inverted lists
. Because of the many documents indexed by such engines and stringent performance requirements imposed by the heavy load of queries, the inverted index stores billions of integers that must be searched efficiently. In this scenario,
index compression
is essential because it leads to a better exploitation of the computer memory hierarchy for faster query processing and, at the same time, allows reducing the number of storage machines.
The aim of this article is twofold: first, surveying the encoding algorithms suitable for inverted index compression and, second, characterizing the performance of the inverted index through experimentation.
Funder
BIGDATAGRAPES
“Algorithms, Data Structures and Combinatorics for Machine Learning”
OK-INSAID
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Cited by
39 articles.
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