The Potential of Learned Index Structures for Index Compression

Oosterhuis, H, Culpepper, J and de Rijke, M 2018, 'The Potential of Learned Index Structures for Index Compression', in Proceedings of the 23rd Annual Australasian Document Computing Symposium, Dunedin, New Zealand, December 11 - 12 2018, pp. 1-4.

Document type: Conference Paper
Collection: Conference Papers

Title The Potential of Learned Index Structures for Index Compression
Author(s) Oosterhuis, H
Culpepper, J
de Rijke, M
Year 2018
Conference name 23rd Australasian Document Computing Symposium (ADCS)
Conference location Dunedin, New Zealand
Conference dates December 11 - 12 2018
Proceedings title Proceedings of the 23rd Annual Australasian Document Computing Symposium
Publisher ACM
Place of publication New York
Start page 1
End page 4
Total pages 4
Abstract Inverted indexes are vital in providing fast key-word-based search. For every term in the document collection, a list of identifiers of documents in which the term appears is stored, along with auxiliary information such as term frequency, and position offsets. While very effective, inverted indexes have large memory requirements for web-sized collections. Recently, the concept of learned index structures was introduced, where machine learned models replace common index structures such as B-tree-indexes, hash-indexes, and bloom-filters. These learned index structures require less memory, and can be computationally much faster than their traditional counterparts. In this paper, we consider whether such models may be applied to conjunctive Boolean querying. First, we investigate how a learned model can replace document postings of an inverted index, and then evaluate the compromises such an approach might have. Second, we evaluate the potential gains that can be achieved in terms of memory requirements. Our work shows that learned models have great potential in inverted indexing, and this direction seems to be a promising area for future research.
Subjects Information Retrieval and Web Search
Data Structures
DOI - identifier 10.1145/3291992.3291993
Copyright notice © 2018 Copyright held by the owner/author(s).
ISBN 9781450365499
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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