Publication Type

Conference Proceeding Article

Version

Publisher’s Version

Publication Date

9-2015

Abstract

The top-k query is a common means to shortlist a number of options from a set of alternatives, based on the user's preferences. Typically, these preferences are expressed as a vector of query weights, defined over the options' attributes. The query vector implicitly associates each alternative with a numeric score, and thus imposes a ranking among them. The top-k result includes the k options with the highest scores. In this context, we define the maximum rank query (MaxRank). Given a focal option in a set of alternatives, the MaxRank problem is to compute the highest rank this option may achieve under any possible user preference, and furthermore, to report all the regions in the query vector's domain where that rank is achieved. MaxRank finds application in market impact analysis, customer profiling, targeted advertising, etc. We propose a methodology for MaxRank processing and evaluate it with experiments on real and benchmark synthetic datasets.

Keywords

Benchmarking, Customer profiling, Market impacts, Maximum rank, Query vectors, Synthetic datasets, Targeted advertising, Top-k query, User's preferences

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Proceedings of the VLDB Endowment: 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii, August 31-September 4, 2015

Volume

8

First Page

1554

Last Page

1565

Identifier

10.14778/2824032.2824053

Publisher

VLDB Endowment

City or Country

Saratoga, CA

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.14778/2824032.2824053

Comments

http://www.vldb.org/pvldb/vol8/p1554-Mouratidis.pdf

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