Efficient Methods for Aggregate Reverse Rank Queries

Yuyang DONG; Hanxiong CHEN; Kazutaka FURUSE; Hiroyuki KITAGAWA

doi:10.1587/transinf.2017DAP0007

Efficient Methods for Aggregate Reverse Rank Queries

Yuyang DONG, Hanxiong CHEN, Kazutaka FURUSE, Hiroyuki KITAGAWA

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Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.4 pp.1012-1020

Publication Date: 2018/04/01

Publicized: 2018/01/18

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017DAP0007

Type of Manuscript: Special Section PAPER (Special Section on Data Engineering and Information Management)

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Yuyang DONG
  University of Tsukuba
Hanxiong CHEN
  University of Tsukuba
Kazutaka FURUSE
  University of Tsukuba
Hiroyuki KITAGAWA
  University of Tsukuba

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Yuyang DONG, Hanxiong CHEN, Kazutaka FURUSE, Hiroyuki KITAGAWA, "Efficient Methods for Aggregate Reverse Rank Queries" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 4, pp. 1012-1020, April 2018, doi: 10.1587/transinf.2017DAP0007.
Abstract: Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017DAP0007/_p

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@ARTICLE{e101-d_4_1012,
author={Yuyang DONG, Hanxiong CHEN, Kazutaka FURUSE, Hiroyuki KITAGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Methods for Aggregate Reverse Rank Queries},
year={2018},
volume={E101-D},
number={4},
pages={1012-1020},
abstract={Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.},
keywords={},
doi={10.1587/transinf.2017DAP0007},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Efficient Methods for Aggregate Reverse Rank Queries
T2 - IEICE TRANSACTIONS on Information
SP - 1012
EP - 1020
AU - Yuyang DONG
AU - Hanxiong CHEN
AU - Kazutaka FURUSE
AU - Hiroyuki KITAGAWA
PY - 2018
DO - 10.1587/transinf.2017DAP0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2018
AB - Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.
ER -