Efficient top-k retrieval with signatures

Authors:
Timothy Chappell;Shlomo Geva;Anthony Nguyen;Guido Zuccon
Affiliations:
Queensland University of Technology, Brisbane, Australia;Queensland University of Technology, Brisbane, Australia;Australian e-Health Research Centre, CSIRO, Brisbane, Australia;Australian e-Health Research Centre, CSIRO, Brisbane, Australia
Venue:
Proceedings of the 18th Australasian Document Computing Symposium
Year:
2013

Citing 6
Cited 0

IR evaluation methods for retrieving highly relevant documents

SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
On the Resemblance and Containment of Documents

SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Detecting near-duplicates for web crawling

Proceedings of the 16th international conference on World Wide Web
A support vector method for optimizing average precision

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Reflective Random Indexing and indirect inference: A scalable method for discovery of implicit connections

Journal of Biomedical Informatics
TOPSIG: topology preserving document signatures

Proceedings of the 20th ACM international conference on Information and knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes a new method of indexing and searching large binary signature collections to efficiently find similar signatures, addressing the scalability problem in signature search. Signatures offer efficient computation with acceptable measure of similarity in numerous applications. However, performing a complete search with a given search argument (a signature) requires a Hamming distance calculation against every signature in the collection. This quickly becomes excessive when dealing with large collections, presenting issues of scalability that limit their applicability. Our method efficiently finds similar signatures in very large collections, trading memory use and precision for greatly improved search speed. Experimental results demonstrate that our approach is capable of finding a set of nearest signatures to a given search argument with a high degree of speed and fidelity.