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An experimental survey of regret minimization query and variants: bridging the best worlds between top-k query and skyline query

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Abstract

When faced with a database containing millions of tuples, a user may be only interested in a (typically much) smaller representative subset. Recently, a query called the regret minimization query was proposed toward this purpose to create such a subset for users. Specifically, this query finds a set of tuples that minimizes the user regret (measured by how far the user’s favorite tuple in the selected set is from his/her favorite tuple in the whole database). The regret minimization query was shown to be very useful in bridging the best worlds between two existing well-known queries, top-k queries and skyline queries: Like top-k queries, the total number of tuples returned in this new query is controllable, and like skyline queries, this new query does not require a user to specify any preference function. Thus, it has attracted a lot of attention from researchers in the database community. Various methods were proposed for regret minimization. However, despite the abundant research effort, there is no systematic comparison among the existing methods. This paper surveys this interesting and evolving research topic by broadly reviewing and comparing the state-of-the-art methods for regret minimization. Moreover, we study different variants of the regret minimization query that has garnered considerable attention in recent years and present some interesting problems that have not yet been addressed in the literature. We implemented 12 state-of-the-art methods published in top-tier venues such as SIGMOD and VLDB from 2010 to 2018 for obtaining regret minimization sets and give an experimental comparison under various parameter settings on both synthetic and real datasets. Our evaluation shows that the optimal choice of methods for regret minimization depends on the application demands. This paper provides an empirical guideline for making such a decision.

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Notes

  1. We define the maximum regret ratio using the supremum instead of the maximum since the function class \(\mathsf {FC}\) can consist of an infinite number of utility functions and a maximum may not exist.

  2. https://www.basketball-reference.com/friv/mvp.html.

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Acknowledgements

The research of Min Xie and Raymond Chi-Wing Wong is supported by HKRGC GRF 16214017.

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Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Min Xie & Raymond Chi-Wing Wong

  2. Denison University, Granville, USA

    Ashwin Lall

  3. Shenzhen Institute of Computing Sciences, Shenzhen University, Shenzhen, China

    Min Xie

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  1. Min Xie
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  2. Raymond Chi-Wing Wong
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Correspondence to Min Xie.

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Xie, M., Wong, R.CW. & Lall, A. An experimental survey of regret minimization query and variants: bridging the best worlds between top-k query and skyline query. The VLDB Journal 29, 147–175 (2020). https://doi.org/10.1007/s00778-019-00570-z

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