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Bound-and-Filter Framework for Aggregate Reverse Rank Queries

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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11250)

Abstract

Finding top-rank products based on a given user’s preference is a user-view rank model that helps users to find their desired products. Recently, another query processing problem named reverse rank query has attracted significant research interest. The reverse rank query is a manufacturer-view model and can find users based on a given product. It can help to target potential users or find the placement for a specific product in marketing analysis.

Unfortunately, previous reverse rank queries only consider one product, and they cannot identify the users for product bundling, which is known as a common sales strategy. To address the limitation, we propose a new query named aggregate reverse rank query to find matching users for a set of products. Three different aggregate rank functions (SUM, MIN, MAX) are proposed to evaluate a given product bundling in a variety of ways and target different users. To resolve these queries more efficiently, we propose a novel and sophisticated bound-and-filter framework. In the bound phase, two points are found to bound the query set for excluding candidates outside the bounds. In the filter phase, two tree-based methods are implemented with the bounds; they are the tree pruning method (TPM) and the double-tree method (DTM). The theoretical analysis and experimental results demonstrate the efficacy of the proposed methods.

Keywords

Similarity search Aggregate reverse rank queries Bound-and-filter Tree-based method 
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Notes

Acknowledgement

This research was partly supported by the program “Research and Development on Real World Big Data Integration and Analysis” of RIKEN, Japan.

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TsukubaIbarakiJapan

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