A new indexing technique for supporting by-attribute membership query of multidimensional data

Wang, Z; Luo, T; Xu, G; Wang, X

A new indexing technique for supporting by-attribute membership query of multidimensional data

Wang, Z Luo, T Xu, G

Wang, X

Permalink

Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7901 LNCS pp. 266 - 277
Issue Date:: 2013-12-01

Closed Access

	Filename	Description	Size
	2013002751OK.pdf		319.61 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Luo, T	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Wang, X	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7901 LNCS pp. 266 - 277	en_US
dc.identifier.isbn	9783642395260	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32424
dc.identifier.uri	http://hdl.handle.net/10453/27892
dc.description.abstract	Multidimensional Data indexing and lookup has been widely used in online data-intensive applications involving in data with multiple attributes. However, there remains a long way to go for the high performance multi-attribute data representation and lookup: the performance of index drops down with the increase of dimensions. In this paper, we present a novel data structure called Bloom Filter Matrix (BFM) to support multidimensional data indexing and by-attribute search. The proposed matrix is based on the Cartesian product of different bloom filters, each representing one attribute of the original data. The structure and parameter of each bloom filter is designed to fit the actual data characteristic and system demand, enabling fast object indexing and lookup, especially by-attribute search of multidimensional data. Experiments show that Bloom Filter Matrix is a fast and accurate data structure for multi-attribute data indexing and by-attribute search with high-correlated queries. © 2013 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-39527-7_27	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A new indexing technique for supporting by-attribute membership query of multidimensional data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7901 LNCS	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7901 LNCS	en_US

Abstract:

Multidimensional Data indexing and lookup has been widely used in online data-intensive applications involving in data with multiple attributes. However, there remains a long way to go for the high performance multi-attribute data representation and lookup: the performance of index drops down with the increase of dimensions. In this paper, we present a novel data structure called Bloom Filter Matrix (BFM) to support multidimensional data indexing and by-attribute search. The proposed matrix is based on the Cartesian product of different bloom filters, each representing one attribute of the original data. The structure and parameter of each bloom filter is designed to fit the actual data characteristic and system demand, enabling fast object indexing and lookup, especially by-attribute search of multidimensional data. Experiments show that Bloom Filter Matrix is a fast and accurate data structure for multi-attribute data indexing and by-attribute search with high-correlated queries. © 2013 Springer-Verlag.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/27892