PROM: efficient matching query processing on high-dimensional data

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Standard

PROM : efficient matching query processing on high-dimensional data. / Ma, Chunyang; Zhou, Yongluan; Shou, Lidan; Chen, Gang.

In: Information Sciences, Vol. 322, 2015, p. 1-19.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Ma, C, Zhou, Y, Shou, L & Chen, G 2015, 'PROM: efficient matching query processing on high-dimensional data', Information Sciences, vol. 322, pp. 1-19. https://doi.org/10.1016/j.ins.2015.05.005

APA

Ma, C., Zhou, Y., Shou, L., & Chen, G. (2015). PROM: efficient matching query processing on high-dimensional data. Information Sciences, 322, 1-19. https://doi.org/10.1016/j.ins.2015.05.005

Vancouver

Ma C, Zhou Y, Shou L, Chen G. PROM: efficient matching query processing on high-dimensional data. Information Sciences. 2015;322:1-19. https://doi.org/10.1016/j.ins.2015.05.005

Author

Ma, Chunyang ; Zhou, Yongluan ; Shou, Lidan ; Chen, Gang. / PROM : efficient matching query processing on high-dimensional data. In: Information Sciences. 2015 ; Vol. 322. pp. 1-19.

Bibtex

@article{4088c761c5044dc78bd739269d80bc87,
title = "PROM: efficient matching query processing on high-dimensional data",
abstract = "Abstract In many applications, such as online dating or job hunting websites, users often need to search for potential matches based on the requirements or preferences imposed by both sides. We refer to this type of queries as matching queries. In spite of their wide applicabilities, there has been little attention devoted to improving their performance. As matching queries often appear in various forms even within a single application, we, in this paper, propose a general processing framework, which can efficiently process various forms of matching queries. Moreover, we illustrate the applicability of this framework by elaborating the detailed processing algorithms of one particular matching query and its extensions to two other forms of matching queries. We conduct an extensive experimental study with both synthetic and real datasets. The results indicate that, for various matching queries, our techniques can highly improve the query performance, especially when the dimensionality is high.",
author = "Chunyang Ma and Yongluan Zhou and Lidan Shou and Gang Chen",
year = "2015",
doi = "10.1016/j.ins.2015.05.005",
language = "English",
volume = "322",
pages = "1--19",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - PROM

T2 - efficient matching query processing on high-dimensional data

AU - Ma, Chunyang

AU - Zhou, Yongluan

AU - Shou, Lidan

AU - Chen, Gang

PY - 2015

Y1 - 2015

N2 - Abstract In many applications, such as online dating or job hunting websites, users often need to search for potential matches based on the requirements or preferences imposed by both sides. We refer to this type of queries as matching queries. In spite of their wide applicabilities, there has been little attention devoted to improving their performance. As matching queries often appear in various forms even within a single application, we, in this paper, propose a general processing framework, which can efficiently process various forms of matching queries. Moreover, we illustrate the applicability of this framework by elaborating the detailed processing algorithms of one particular matching query and its extensions to two other forms of matching queries. We conduct an extensive experimental study with both synthetic and real datasets. The results indicate that, for various matching queries, our techniques can highly improve the query performance, especially when the dimensionality is high.

AB - Abstract In many applications, such as online dating or job hunting websites, users often need to search for potential matches based on the requirements or preferences imposed by both sides. We refer to this type of queries as matching queries. In spite of their wide applicabilities, there has been little attention devoted to improving their performance. As matching queries often appear in various forms even within a single application, we, in this paper, propose a general processing framework, which can efficiently process various forms of matching queries. Moreover, we illustrate the applicability of this framework by elaborating the detailed processing algorithms of one particular matching query and its extensions to two other forms of matching queries. We conduct an extensive experimental study with both synthetic and real datasets. The results indicate that, for various matching queries, our techniques can highly improve the query performance, especially when the dimensionality is high.

U2 - 10.1016/j.ins.2015.05.005

DO - 10.1016/j.ins.2015.05.005

M3 - Journal article

VL - 322

SP - 1

EP - 19

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -

ID: 179278157