Efficient computation of regret-ratio minimizing set: A compact maxima representative

Abolfazl Asudeh, Azade Nazi, Nan Zhang, Gautam Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Finding the maxima of a database based on a user preference, especially when the ranking function is a linear combination of the attributes, has been the subject of recent research. A critical observation is that the convex hull is the subset of tuples that can be used to find the maxima of any linear function. However, in real world applications the convex hull can be a significant portion of the database, and thus its performance is greatly reduced. Thus, computing a subset limited to r tuples that minimizes the regret ratio (a measure of the user's dissatisfaction with the result from the limited set versus the one from the entire database) is of interest. In this paper, we make several fundamental theoretical as well as practical advances in developing such a compact set. In the case of two dimensional databases, we develop an optimal linearithmic time algorithm by leveraging the ordering of skyline tuples. In the case of higher dimensions, the problem is known to be NP-complete. As one of our main results of this paper, we develop an approximation algorithm that runs in linearithmic time and guarantees a regret ratio, within any arbitrarily small user-controllable distance from the optimal regret ratio. The comprehensive set of experiments on both synthetic and publicly available real datasets confirm the efficiency, quality of output, and scalability of our proposed algorithms.

Original languageEnglish (US)
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages821-834
Number of pages14
ISBN (Electronic)9781450341974
DOIs
StatePublished - May 9 2017
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Chicago, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
VolumePart F127746
ISSN (Print)0730-8078

Other

Other2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
CountryUnited States
CityChicago
Period5/14/175/19/17

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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    Asudeh, A., Nazi, A., Zhang, N., & Das, G. (2017). Efficient computation of regret-ratio minimizing set: A compact maxima representative. In SIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data (pp. 821-834). (Proceedings of the ACM SIGMOD International Conference on Management of Data; Vol. Part F127746). Association for Computing Machinery. https://doi.org/10.1145/3035918.3035932