research-article

Processing a large number of continuous preference top-k queries

Authors:

Pankaj K. Agarwal,

Jun YangAuthors Info & Claims

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

Pages 397 - 408

https://doi.org/10.1145/2213836.2213882

Published: 20 May 2012 Publication History

Abstract

Given a set of objects, each with multiple numeric attributes, a (preference) top-k query retrieves the k objects with the highest scores according to a user preference, defined as a linear combination of attribute values. We consider the problem of processing a large number of continuous top-k queries, each with its own preference. When objects or user preferences change, the query results must be updated. We present a dynamic index that supports the reverse top k query, which is of independent interest. Combining this index with another one for top-k queries, we develop a scalable solution for processing many continuous top-k queries that exploits the clusteredness in user preferences. We also define an approximate version of the problem and present a solution significantly more efficient than the exact one with little loss in accuracy.

References

[1]

P. Afshani and T.M. Chan. Optimal halfspace range reporting in three dimensions. In SODA '09, pages 180--186, 2009.

Digital Library

[2]

P. K. Agarwal, L. Arge, J. Erickson, P. G. Franciosa, and J. S. Vitter. Efficient searching with linear constraints. J. Comput. Syst. Sci., 61(2):194--216, 2000.

Digital Library

[3]

P. K. Agarwal, S. Har-Peled, and K. R. Varadarajan. Approximating extent measures of points. Journal of the ACM, 51:606--635, 2004.

Digital Library

[4]

P. K. Agarwal, S. Har-Peled, and K. R. Varadarajan. Geometric approximation via coresets. In J. E. Goodman, J. Pach, and E.Welzl, editors, Combinatorial and Computational Geometry, pages 1--30. Cambridge University Press, New York, 2005.

[5]

P. K. Agarwal, S. Har-Peled, and H. Yu. Robust shape fitting via peeling and grating coresets. In SODA '06, pages 182--191, 2006.

Digital Library

[6]

P. K. Agarwal and J. Matouaek. Dynamic half-space range reporting and its applications. Algorithmica, pages 325--345, 1995.

[7]

P. K. Agarwal, J. M. Phillips, and H. Yu. Stability of epsilon kernels. In ESA'10, pages 487--499, 2010. Springer-Verlag.

Digital Library

[8]

P. K. Agarwal and M. Sharir. Arrangements and their applications. In Handbook of Computational Geometry, pages 49--119. Elsevier Science Publishers B.V. North-Holland, 1998.

[9]

S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu. An optimal algorithmfor approximate nearest neighbor searching fixed dimensions. J. ACM, 45(6):891--923, 1998.

Digital Library

[10]

G. Barequet and S. Har-Peled. Efficiently approximating the minimum-volume bounding box of a point set in three dimensions. In SODA '99, pages 82--91, Philadelphia, PA, USA, 1999.

Digital Library

[11]

S. Börzsönyi, D. Kossmann, and K. Stocker. The skyline operator. In ICDE'01, pages 421--430, Washington, DC, USA, 2001.

Digital Library

[12]

H. Brönnimann, B. Chazelle, and J. Pach. How hard is half-space range searching. Discrete & Comput. Geom., 10:143--155, 1993.

Digital Library

[13]

T. M. Chan. Three problems about dynamic convex hulls. In SoCG'11, pages 27--36, New York, NY, USA, 2011.

Digital Library

[14]

B. Chandramouli, J. Xie, and J. Yang. On the database/network interface in large-scale publish/subscribe systems. In SIGMOD'06, pages 587--598, Chicago, Illinois, USA, June 2006.

Digital Library

[15]

Y.-C. Chang, L. Bergman, V. Castelli, C.-S. Li, M.-L. Lo, and J. R. Smith. The onion technique: indexing for linear optimization queries. SIGMOD Rec., 29:391--402, May 2000.

Digital Library

[16]

B. Chazelle, L. J. Guibas, and D. T. Lee. The power of geometric duality. BIT, 25:76--90, June 1985.

Digital Library

[17]

G. Das, D. Gunopulos, N. Koudas, and N. Sarkas. Ad-hoc top-k query answering for data streams. In VLDB'07, pages 183--194, 2007.

Digital Library

[18]

G. Das, D. Gunopulos, N. Koudas, and D. Tsirogiannis. Answering top-k queries using views. In VLDB '06, pages 451--462. 2006.

Digital Library

[19]

V. Hristidis, N. Koudas, and Y. Papakonstantinou. Prefer: a system for the efficient execution of multi-parametric ranked queries. SIGMOD Rec., 30:259--270, May 2001.

Digital Library

[20]

I. F. Ilyas, G. Beskales, and M. A. Soliman. A survey of top-k query processing techniques in relational database systems. ACM Computing Surveys, 40(4), 2008.

Digital Library

[21]

M. Jacob and Z. G. Ives. Sharing work in keyword search over databases. In SIGMOD '11, pages 577--588, 2011.

Digital Library

[22]

J. Matousek. Reporting points in halfspaces. Comput. Geom., 2:169--186, 1992.

Digital Library

[23]

J. Matousek. Lectures on Discrete Geometry. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2002.

Digital Library

[24]

K.Mouratidis, S. Bakiras, and D. Papadias. Continuous monitoring of top-k queries over sliding windows. In SIGMOD '06, pages 635--646, New York, NY, USA, 2006.

Digital Library

[25]

D. Papadias and Y. Tao. Reverse nearest neighbor query. In L. Liu and M. T. Özsu, editors, Encyclopedia of Database Systems, pages 2434--2438. Springer, 2009.

[26]

M. A. Soliman, I. F. Ilyas, D. Martinenghi, and M. Tagliasacchi. Ranking with uncertain scoring functions: semantics and sensitivity measures. In SIGMOD '11, pages 805--816, 2011.

Digital Library

[27]

P. Tsaparas, N. Koudas, Y. Kotidis, T. Palpanas, and D. Srivastava. Ranked join indices. In ICDE '03, pages 277--288, 2003.

[28]

A. Vlachou, C. Doulkeridis, Y. Kotidis, and K. Nørvåg. Reverse top-k queries. In ICDE '10, pages 365--376, 2010.

[29]

A. Vlachou, C. Doulkeridis, Y. Kotidis, and K. Nørvåg. Monochromatic and bichromatic reverse top-k queries. IEEE Trans. Knowl. Data Eng., 23(8):1215--1229, 2011.

Digital Library

[30]

H. Yu, P. K. Agarwal, R. Poreddy, and K. R. Varadarajan. Practical methods for shape fitting and kinetic data structures using core sets. In SCG '04, pages 263--272, 2004.

Digital Library

Cited By

Bian ZYan XZhang JYiu MTang B(2024)QSRP: Efficient Reverse $k-\text{Ranks}$ Query Processing on High-Dimensional Embeddings2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00351(4614-4627)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00351
Zhu YChen LGao YMa RZheng BZhao J(2024)HJG: An Effective Hierarchical Joint Graph for ANNS in Multi-Metric Spaces2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00326(4275-4287)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00326
Zhu RJia YYang XZheng BWang BZong C(2024)Multiple Continuous Top-K Queries Over Data Stream2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00129(1575-1588)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00129
Show More Cited By

Index Terms

Processing a large number of continuous preference top-k queries
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

Recommendations

Durable reverse top-k queries on time-varying preference
Abstract
Recently, a query, called reverse top- $k$ query, is proposed. The reverse top- $k$ query takes an object as input and retrieves the users whose top- $k$ query results include the object while the top- $k$ query retrieves the top- $k$ matching objects based on ...
Time- and Space-Efficient Sliding Window Top-k Query Processing

A sliding window top-k (top-k/w) query monitors incoming data stream objects within a sliding window of size w to identify the k highest-ranked objects with respect to a given scoring function over time. Processing of such queries is challenging because,...
Scalable and efficient processing of top-k multiple-type integrated queries
Abstract
In this paper, we define a new class of queries, the top-k multiple-type integrated query (simply, top-k MULTI query). It deals with multiple data types and finds the information in the order of relevance between the query and the object. Various ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '12: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

May 2012

886 pages

ISBN:9781450312479

DOI:10.1145/2213836

General Chairs:
K. Selçuk Candan
Arizona State University
,
Yi Chen
Arizona State University
,
Richard Snodgrass
University of Arizona
,
Program Chair:
Luis Gravano
Columbia University
,
Publications Chair:
Ariel Fuxman
Microsoft Research

Copyright © 2012 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]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 May 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGMOD/PODS '12

Sponsor:

SIGMOD

SIGMOD/PODS '12: International Conference on Management of Data

May 20 - 24, 2012

Arizona, Scottsdale, USA

Acceptance Rates

SIGMOD '12 Paper Acceptance Rate 48 of 289 submissions, 17%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

45
Total Citations
View Citations
880
Total Downloads

Downloads (Last 12 months)28
Downloads (Last 6 weeks)1

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Bian ZYan XZhang JYiu MTang B(2024)QSRP: Efficient Reverse $k-\text{Ranks}$ Query Processing on High-Dimensional Embeddings2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00351(4614-4627)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00351
Zhu YChen LGao YMa RZheng BZhao J(2024)HJG: An Effective Hierarchical Joint Graph for ANNS in Multi-Metric Spaces2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00326(4275-4287)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00326
Zhu RJia YYang XZheng BWang BZong C(2024)Multiple Continuous Top-K Queries Over Data Stream2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00129(1575-1588)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00129
Zhang CLi JJiang S(2024)Durable reverse top-k queries on time-varying preferenceWorld Wide Web10.1007/s11280-024-01293-027:5Online publication date: 2-Aug-2024
https://doi.org/10.1007/s11280-024-01293-0
Zhang ZGan JBao ZKazemi SChen GZhu FIves ZBonifati AEl Abbadi A(2022)Approximate Range ThresholdingProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526123(1108-1121)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526123
Guo HLi JGao H(2022)Minimum Epsilon-Kernel Computation for Large-Scale Data ProcessingJournal of Computer Science and Technology10.1007/s11390-022-2429-637:6(1398-1411)Online publication date: 30-Nov-2022
https://doi.org/10.1007/s11390-022-2429-6
Tang BMouratidis KHan MLi GLi ZIdreos SSrivastava D(2021)On m-Impact Regions and Standing Top-k Influence ProblemsProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452832(1784-1796)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452832
Wang YLi YWong RTan K(2021)A Fully Dynamic Algorithm for k-Regret Minimizing Sets2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00144(1631-1642)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00144
Wang YZhu LMa JHu GLiu JQiao Y(2021)Knowledge Graph-Based Spatial-Aware User Community Preference Query Algorithm for LBSNsBig Data Research10.1016/j.bdr.2020.10016923(100169)Online publication date: Feb-2021
https://doi.org/10.1016/j.bdr.2020.100169
Nikitopoulos PSfyris GVlachou ADoulkeridis CTelelis O(2020)Pruning techniques for parallel processing of reverse top-k queriesDistributed and Parallel Databases10.1007/s10619-020-07297-9Online publication date: 25-May-2020
https://doi.org/10.1007/s10619-020-07297-9
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten