research-article

Ranking with uncertain scoring functions: semantics and sensitivity measures

Authors:

Mohamed A. Soliman,

Davide Martinenghi,

Marco TagliasacchiAuthors Info & Claims

SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

Pages 805 - 816

https://doi.org/10.1145/1989323.1989408

Published: 12 June 2011 Publication History

Abstract

Ranking queries report the top-K results according to a user-defined scoring function. A widely used scoring function is the weighted summation of multiple scores. Often times, users cannot precisely specify the weights in such functions in order to produce the preferred order of results. Adopting uncertain/incomplete scoring functions (e.g., using weight ranges and partially-specified weight preferences) can better capture user's preferences in this scenario.

In this paper, we study two aspects in uncertain scoring functions. The first aspect is the semantics of ranking queries, and the second aspect is the sensitivity of computed results to refinements made by the user. We formalize and solve multiple problems under both aspects, and present novel techniques that compute query results efficiently to comply with the interactive nature of these problems.

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Cited By

Martinenghi D(2025)Parallelizing the Computation of Grid Resistance to Measure the Strength of Skyline TuplesAlgorithms10.3390/a1801002918:1(29)Online publication date: 7-Jan-2025
https://doi.org/10.3390/a18010029
Mouratidis KLi KTang B(2024)Marrying Top-k with Skyline Queries: Operators with Relaxed Preference Input and Controllable Output SizeACM Transactions on Database Systems10.1145/370572650:1(1-37)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3705726
Xiao XLi J(2023)rkHit: Representative Query with Uncertain PreferenceProceedings of the ACM on Management of Data10.1145/35892711:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589271
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Index Terms

Ranking with uncertain scoring functions: semantics and sensitivity measures
1. Information systems
  1. Data management systems
    1. Database management system engines

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Published In

cover image ACM Conferences

SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

June 2011

1364 pages

ISBN:9781450306614

DOI:10.1145/1989323

General Chair:
Timos Sellis
IMIS/RC Athena
,
Program Chair:
Renée J. Miller
University of Toronto
,
Publications Chairs:
Anastasios Kementsietsidis
IBM T.J. Watson Research Center
,
Yannis Velegrakis
University of Trento

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2011

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SIGMOD/PODS '11

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SIGMOD

SIGMOD/PODS '11: International Conference on Management of Data

June 12 - 16, 2011

Athens, Greece

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Martinenghi D(2025)Parallelizing the Computation of Grid Resistance to Measure the Strength of Skyline TuplesAlgorithms10.3390/a1801002918:1(29)Online publication date: 7-Jan-2025
https://doi.org/10.3390/a18010029
Mouratidis KLi KTang B(2024)Marrying Top-k with Skyline Queries: Operators with Relaxed Preference Input and Controllable Output SizeACM Transactions on Database Systems10.1145/370572650:1(1-37)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3705726
Xiao XLi J(2023)rkHit: Representative Query with Uncertain PreferenceProceedings of the ACM on Management of Data10.1145/35892711:2(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589271
Munoz DPinto MFuentes L(2023)Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learningKnowledge-Based Systems10.1016/j.knosys.2023.110558270:COnline publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110558
Mouratidis KLi KTang B(2023)Quantifying the competitiveness of a dataset in relation to general preferencesThe VLDB Journal10.1007/s00778-023-00804-133:1(231-250)Online publication date: 8-Aug-2023
https://doi.org/10.1007/s00778-023-00804-1
Mouratidis KLi KTang BLi GLi ZIdreos SSrivastava D(2021)Marrying Top-k with Skyline Queries: Relaxing the Preference Input while Producing Output of Controllable SizeProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457299(1317-1330)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3457299
Gale AMarian A(2020)Explaining monotonic ranking functionsProceedings of the VLDB Endowment10.14778/3436905.343692214:4(640-652)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.14778/3436905.3436922
Ciaccia PMartinenghi D(2020)Flexible SkylinesACM Transactions on Database Systems10.1145/340611345:4(1-45)Online publication date: 10-Dec-2020
https://dl.acm.org/doi/10.1145/3406113
Tang BMouratidis KYiu MChen Z(2019)Creating top ranking options in the continuous option and preference spaceProceedings of the VLDB Endowment10.14778/3339490.333950012:10(1181-1194)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.14778/3339490.3339500
Mouratidis K(2019)Geometric Top-k Processing: Updates Since MDM'16 [Advanced Seminar]2019 20th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM.2019.00-81(1-3)Online publication date: Jun-2019
https://doi.org/10.1109/MDM.2019.00-81
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