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Unifying Qualitative and Quantitative Database Preferences to Enhance Query Personalization

DocUID: 2015-003 Full Text: PDF

Author: Roxana Gheorghiu, Alexandros Labrinidis, Panos K. Chrysanthis

Abstract: Query personalization can be an effective technique in dealing with the data scalability challenge, primarily from the human point of view, i.e., making big data easier to use. In order to customize their query results, users need to express their preferences in a simple and user-friendly manner. In this paper, we present a graph-based theoretical framework and a prototype system that unify qualitative and quantitative preferences, while eliminating their disadvantages. Our integrated system allows for (1) the specification of database preferences and the creation of user preference profiles in a user-friendly manner, (2) the manipulation of preferences of individuals or groups of users and (3) total ordering of the tuples in the database, matching both qualitative and quantitative preferences, hence significantly increasing the number of tuples covered by the user preferences. We confirmed the latter experimentally by comparing our preference selection algorithm with Fagin’s TA algorithm.

Published In: 2nd International Workshop on Exploratory Search in Databases and the Web

Pages: 6-8

Year Published: 2015

Note: Co-located with ACM SIGMOD 2015

Project: AstroSelf Subject Area: database preferences

Publication Type: Workshop Paper

Sponsor: NSF CAREER IIS-0746696, NSF OIA-1028162

Citation:Text Latex BibTex XML Roxana Gheorghiu, Alexandros Labrinidis, and Panos K. Chrysanthis. Unifying Qualitative and Quantitative Database Preferences to Enhance Query Personalization. 2nd International Workshop on Exploratory Search in Databases and the Web. 6-8. 2015. (Note: Co-located with ACM SIGMOD 2015).