Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
article

Branch-and-bound processing of ranked queries

Published: 01 May 2007 Publication History

Abstract

Despite the importance of ranked queries in numerous applications involving multi-criteria decision making, they are not efficiently supported by traditional database systems. In this paper, we propose a simple yet powerful technique for processing such queries based on multi-dimensional access methods and branch-and-bound search. The advantages of the proposed methodology are: (i) it is space efficient, requiring only a single index on the given relation (storing each tuple at most once), (ii) it achieves significant (i.e., orders of magnitude) performance gains with respect to the current state-of-the-art, (iii) it can efficiently handle data updates, and (iv) it is applicable to other important variations of ranked search (including the support for non-monotone preference functions), at no extra space overhead. We confirm the superiority of the proposed methods with a detailed experimental study.

References

[1]
Y. Chang, L. Bergman, V. Castelli, C. Li, M. Lo, J. Smith. The onion technique: indexing for linear optimization queries, ACM SIGMOD, 2000.
[2]
V. Hristidis, N. Koudas, Y. Papakonstantinou. PREFER: a system for the efficient execution of multi-parametric ranked queries, ACM SIGMOD, 2001.
[3]
P. Tsaparas, T. Palpanas, Y. Kotidis, N. Koudas, D. Srivastava. Ranked join indices, ICDE, 2003.
[4]
Hristidis, V. and Papakonstantinou, Y., Algorithms and applications for answering ranked queries using ranked views. VLDB J. v13 i1. 49-70.
[5]
A. Guttman, R-trees: a dynamic index structure for spatial searching, ACM SIGMOD, 1984.
[6]
N. Beckmann, H. Kriegel, R. Schneider, B. Seeger, The R*-tree: an efficient and robust access method for points and rectangles, ACM SIGMOD, 1990.
[7]
Bruno, N., Chaudhuri, S. and Gravano, L., Top-k selection queries over relational databases: mapping strategies and performance evaluation. TODS. v27 i2. 153-187.
[8]
N. Bruno, L. Gravano, A. Marian, Evaluating top-k queries over web-accessible databases, ICDE, 2002.
[9]
I. Ilyas, W. Aref, A. Elmagarmid, Joining ranked inputs in practice, VLDB, 2002.
[10]
K. Chang, S.-W. Hwang, Minimal probing: supporting expensive predicates for top-k queries, SIGMOD, 2002.
[11]
N. Roussopoulos, S. Kelly, F. Vincent, Nearest neighbor queries, ACM SIGMOD, 1995.
[12]
Hjaltason, G. and Samet, H., Distance browsing in spatial databases. ACM TODS. v24 i2. 265-318.
[13]
C. Böhm, H. Kriegel, Determining the convex hull in large multidimensional databases, DAWAK, 2001.
[14]
D. Kossmann, F. Ramsak, S. Rost, Shooting stars in the sky: an online algorithm for skyline queries, VLDB, 2002.
[15]
D. Papadias, Y. Tao, G. Fu, B. Seeger, An optimal and progressive algorithm for skyline queries, ACM SIGMOD, 2003.
[16]
R. Benetis, C. Jensen, G. Karciauskas, S. Saltenis, Nearest neighbor and reverse nearest neighbor queries for moving objects, IDEAS, 2002.
[17]
S. Berchtold, C. Böhm, D. Keim, H. Kriegel, A cost model for nearest neighbor search in high-dimensional data spaces, ACM PODS, 1997.
[18]
A. Papadopoulos, Y. Manolopoulos, Performance of nearest neighbor queries in R-trees, ICDT, 1997.
[19]
Boehm, C., A cost model for query processing in high dimensional data spaces. ACM TODS. v25 i2. 129-178.
[20]
Y. Theodoridis, T. Sellis, A model for the prediction of R-tree performance, ACM PODS, 1996.
[21]
M. Muralikrishna, D. DeWitt, Equi-depth histograms for estimating selectivity factors for multi-dimensional queries, ACM SIGMOD, 1988.
[22]
S. Acharya, V. Poosala, S. Ramaswamy, Selectivity estimation in spatial databases, ACM SIGMOD, 1999.
[23]
N. Bruno, L. Gravano, S. Chaudhuri, STHoles: a workload aware multidimensional histogram, ACM SIGMOD, 2001.
[24]
D. Gunopulos, G. Kollios, V. Tsotras, C. Domeniconi, Approximate multi-dimensional aggregate range queries over real attributes, ACM SIGMOD, 2000.
[25]
Press, W., Flannery, B., Teukolsky, S. and Vetterling, W., Numerical Recipes in C. ISBN:0-521-75033-4. Cambridge University Press, Cambridge.
[26]
Zipf, G., Human Behaviour and the Principle of Least Effort: An Introduction to Human Ecology. Addison-Wesley, Reading, MA.
[27]
N. Katayama, S. Satoh, The SR-tree: an index structure for high-dimensional nearest neighbor queries, ACM SIGMOD, 1997.
[28]
S. Berchtold, D. Keim, H.P. Kriegel, The X-tree: an index structure for high-dimensional data, VLDB, 1996.
[29]
Y. Sakurai, M. Yoshikawa, S. Uemura, H. Kojima, The A-tree: an index structure for high-dimensional spaces using relative approximation, VLDB, 2000.

Cited By

View all
  • (2024)Quantifying the competitiveness of a dataset in relation to general preferencesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00804-133:1(231-250)Online publication date: 1-Jan-2024
  • (2022)T-LevelIndex: Towards Efficient Query Processing in Continuous Preference SpaceProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526182(2149-2162)Online publication date: 10-Jun-2022
  • (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
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 May 2007

Author Tags

  1. Branch-and-bound algorithms
  2. Databases
  3. R-tree
  4. Ranked queries

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Oct 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Quantifying the competitiveness of a dataset in relation to general preferencesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00804-133:1(231-250)Online publication date: 1-Jan-2024
  • (2022)T-LevelIndex: Towards Efficient Query Processing in Continuous Preference SpaceProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526182(2149-2162)Online publication date: 10-Jun-2022
  • (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
  • (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
  • (2021)Optimal Placement of Taxis in a City Using Dominating Set ProblemDatabases Theory and Applications10.1007/978-3-030-69377-0_10(111-124)Online publication date: 29-Jan-2021
  • (2020)Pruning techniques for parallel processing of reverse top-k queriesDistributed and Parallel Databases10.1007/s10619-020-07297-939:1(169-199)Online publication date: 25-May-2020
  • (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
  • (2018)Weighted Aggregate Reverse Rank QueriesACM Transactions on Spatial Algorithms and Systems10.1145/32252164:2(1-23)Online publication date: 10-Aug-2018
  • (2017)Exploratory product search using top-k join queriesInformation Systems10.1016/j.is.2016.09.00464:C(75-92)Online publication date: 1-Mar-2017
  • (2017)Subscription-based data aggregation techniques for top-k monitoring queriesWorld Wide Web10.1007/s11280-016-0385-120:2(237-265)Online publication date: 1-Mar-2017
  • Show More Cited By

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media