Article

Free access

Spatial join selectivity using power laws

Authors:

Christos Faloutsos,

Bernhard Seeger,

Caetano Traina, Jr.Authors Info & Claims

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data

Pages 177 - 188

https://doi.org/10.1145/342009.335412

Published: 16 May 2000 Publication History

Abstract

We discovered a surprising law governing the spatial join selectivity across two sets of points. An example of such a spatial join is “find the libraries that are within 10 miles of schools”. Our law dictates that the number of such qualifying pairs follows a power law, whose exponent we call “pair-count exponent” (PC). We show that this law also holds for self-spatial-joins (“find schools within 5 miles of other schools”) in addition to the general case that the two point-sets are distinct. Our law holds for many real datasets, including diverse environments (geographic datasets, feature vectors from biology data, galaxy data from astronomy).

In addition, we introduce the concept of the Box-Occupancy-Product-Sum (BOPS) plot, and we show that it can compute the pair-count exponent in a timely manner, reducing the run time by orders of magnitude, from quadratic to linear. Due to the pair-count exponent and our analysis (Law 1), we can achieve accurate selectivity estimates in constant time (O(1)) without the need for sampling or other expensive operations. The relative error in selectivity is about 30% with our fast BOPS method, and even better (about 10%), if we use the slower, quadratic method.

References

[1]

L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, J.S. Vitter - "Scalable Sweeping-Based Spatial Join". VLDB 1998, pp. 570-581.

Digital Library

[2]

A.Belussi and C. Faloutsos- "Estimating the Selectivity of Spatial Queries Using the 'Correlation' Fractal Dimension". VLDB 1995, pp. 299-310.

Digital Library

[3]

T. Brinkhoff, H. P. Kriegel, B. Seeger - "Efficient Processing of Spatial Joins using R-trees", SIGMOD 1993. pp. 237-246.

Digital Library

[4]

J. Van den Bercken, B. Seeger, P. Widmayer - "The Bulk Index Join: A Generic Approach to Processing Non-Equijoins". ICDE 1999, pp. 257.

Digital Library

[5]

Bureau of the Census - Tiger#Line Precensus Files: 1990 technical documentation. Bureau of the Census. Washington, D.C. 1989.

[6]

S. Christodoulakis- "Implications of Certain Assumptions in Database Performance Evaluation". TODS 9(2), 1984, pp. 163-186.

Digital Library

[7]

S. Chaudhuri, R. Motwani, V. R. Narasayya - "On Random Sampling over Joins". SIGMOD 1999, pp. 263-274.

Digital Library

[8]

D. J. DeWitt, J. F. Naughton, D. A. Schneider - "An Evaluation of Non-Equijoin Algorithms". VLDB 1991, pp. 443-452.

Digital Library

[9]

C. Faloutsos, H.V. Jagadish and N. Sidiropoulos - "Information Recovery from Partial data". Tech. Report ISR-TR-97-7, Inst. For Systems Research, Univ. of Maryland, College Park, MD, 1997.

[10]

C. Faloutsos, I. Kamel -"Beyond Uniformity and Independence: Analysis of R-trees Using the Concept of Fractal Dimension". PODS 1994, pp. 4-13.

Digital Library

[11]

C. Faloutsos, M. Ranganathan, Y. Manolopoulos - "Fast Subsequence Matching in Time-Series Databases". SIGMOD 1994, pp. 419-429.

Digital Library

[12]

O. Gfinther - "Efficient Computation of Spatial Joins". ICDE 1993, pp. 50-59.

Digital Library

[13]

R. H. Gfiting - "An Introduction to Spatial Database Systems". The VLDB Journal. 3(4). October 1994. pp. 357-399.

Digital Library

[14]

K. H#it6nen, M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen- "Knowledge Discovery from Telecommunication Network Alarm Databases". ICDE 1996, pp. 115-122.

Digital Library

[15]

N. Koudas, K.C. Sevcik, - "Size Separation Spatial Join". SIGMOD 1997, 324-335.

Digital Library

[16]

N. Koudas, K. C. Sevcik, - "High Dimensional Similarity Joins: Algorithms and Performance Evaluation". ICDE 1998, pp. 466-475.

Digital Library

[17]

A. Christian Kvnig, G. Weikum- "Combining Histograms and Parametric Curve Fitting for Feedback-Driven Query Result-size Estimation".VLDB 1999, pp.423-434.

Digital Library

[18]

M.-L.Lo, C. V. Ravishankar- "Spatial Joins using Seeded Trees". SIGMOD 1994, pp. 209-220.

Digital Library

[19]

N. Mamoulis, D. Papadias - "Integration of Spatial Join Algorithms for Processing Multiple Inputs". SIGMOD 1999. pp.l-12.

Digital Library

[20]

H. Mannila, H. Toivonen, A. I. Verkamo - "Discovering Frequent Episodes in Sequences". KDD 1995, pp.210-215.

[21]

R. T. Ng, J, Han- "Efficient and Effective Clustering Methods for Spatial Data Mining". VLDB 1994, pp. 144-155.

Digital Library

[22]

J. Orenstein, - "Spatial Query Processing in an Object-Oriented Database System". SIGMOD 1986, pp. 326-33.

Digital Library

[23]

J. M. Patel, D. J. DeWitt, - "Partition Based Spatial-Merge Join". SIGMOD 1996, pp. 259-270.

Digital Library

[24]

V. Poosala - "Histogramm-based estimation techniques in databases". PhD thesis, Univ. of Wisconsin-Madison, 1997.

Digital Library

[25]

D. Papadias, N. Mamoulis, Y. Theodoridis - "Processing and Optimization of Multiway Spatial Joins Using R-Trees". PODS 1999, pp 44-55.

Digital Library

[26]

P. G. Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, T. T. Price, - "Access Path Selection in a Relational Database Management System". SIGMOD 1979, pp. 23-34.

Digital Library

[27]

D. W. Scott - Multivariate Density Estimation, Wiley & Sons 1992.

[28]

B. W. Silverman - Density Estimation for Statistics and Data Analysis. Chapman & Hall 1986.

[29]

K. C. Sevcik, N. Koudas - "Filter Trees for Managing Spatial Data over a Range of Size Granularities". VLDB 1996, pp. 16-27.

Digital Library

[30]

K. Shim, R. Srikant, R.Agrawal - "High- Dimensional Similarity Joins". ICDE 1997. pp. 301- 311.

Digital Library

[31]

M. Turk and A. Pentland- "Eigenfaces for Recognition". Journal of cognitive Neuroscience, vol 3(1), 1991, pp. 71-86.

Digital Library

[32]

Y. Theodoridis, T. K. Sellis - "A Model for the Prediction of R-tree Performance". PODS 1996, pp.161-171.

Digital Library

[33]

Y. Theodoridis, E. Stefanakis, T. K. Sellis - "Cost Models for Join Queries in Spatial Databases". ICDE 1998, pp. 476-483.

Digital Library

[34]

H. D. Wactlar, T. Kanade, M.A. Smith and S. M. Stevens - "Intelligente Access to Digital Video: Informedia Project". IEEE Computer, vo129(3), pp. 46-52, May 1996.

Digital Library

[35]

J. S. Vitter, M. Wang- "Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets". SIGMOD 1999, pp. 193-204.

Digital Library

Cited By

Belussi AMigliorini SEldawy A(2024)A Generic Machine Learning Model for Spatial Query Optimization based on Spatial EmbeddingsACM Transactions on Spatial Algorithms and Systems10.1145/365763310:4(1-33)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3657633
Shi YShi Y(2022)Feature SelectionAdvances in Big Data Analytics10.1007/978-981-16-3607-3_4(249-304)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-981-16-3607-3_4
Qi SBouros PMamoulis N(2020)Top-k spatial distance joinsGeoInformatica10.1007/s10707-020-00393-z24:3(591-631)Online publication date: 12-Feb-2020
https://doi.org/10.1007/s10707-020-00393-z
Show More Cited By

Index Terms

Spatial join selectivity using power laws
1. Information systems
  1. Data management systems
    1. Database management system engines
  2. Information systems applications

Recommendations

Spatial join selectivity using power laws

We discovered a surprising law governing the spatial join selectivity across two sets of points. An example of such a spatial join is “find the libraries that are within 10 miles of schools”. Our law dictates that the number of such qualifying pairs ...
Self-spacial join selectivity estimation using fractal concepts

The problem of selectivity estimation for queries of nontraditional databases is still an open issue. In this article, we examine the problem of selectivity estimation for some types of spatial queries in databases containing real data. We have shown ...
Multi-way spatial join selectivity for the ring join graph

Efficient spatial query processing is very important since the applications of the spatial DBMS (e.g. GIS, CAD/CAM, LBS) handle massive amount of data and consume much time. Many spatial queries contain the multi-way spatial join due to the fact that ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data

May 2000

604 pages

ISBN:1581132174

DOI:10.1145/342009

Chairmen:
Maggie Dunham
Southern Methodist Univ.
,
Jeffrey F. Naughton
Univ. of Wisconsin-Madison
,
Weidong Chen
Southern Methodist Univ.
,
Nick Koudas
AT &T Labs

ACM SIGMOD Record Volume 29, Issue 2
June 2000
609 pages
ISSN:0163-5808
DOI:10.1145/335191
Editors:
Weidong Chen
Southern Methodist Univ., Dallas, TX
,
Jeffrey Naughton
Univ. of Wisconsin-Madison, Madison
,
Philip A. Bernstein
Microsoft
Issue’s Table of Contents

Copyright © 2000 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: 16 May 2000

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

SIGMOD/PODS00

Sponsor:

SIGMOD

SIGMOD/PODS00: ACM International Conference on Management of Data and Symposium on Principles of Database Systems

May 15 - 18, 2000

Texas, Dallas, USA

Acceptance Rates

SIGMOD '00 Paper Acceptance Rate 42 of 248 submissions, 17%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

68
Total Citations
View Citations
234
Total Downloads

Downloads (Last 12 months)94
Downloads (Last 6 weeks)12

Reflects downloads up to 27 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Belussi AMigliorini SEldawy A(2024)A Generic Machine Learning Model for Spatial Query Optimization based on Spatial EmbeddingsACM Transactions on Spatial Algorithms and Systems10.1145/365763310:4(1-33)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3657633
Shi YShi Y(2022)Feature SelectionAdvances in Big Data Analytics10.1007/978-981-16-3607-3_4(249-304)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-981-16-3607-3_4
Qi SBouros PMamoulis N(2020)Top-k spatial distance joinsGeoInformatica10.1007/s10707-020-00393-z24:3(591-631)Online publication date: 12-Feb-2020
https://doi.org/10.1007/s10707-020-00393-z
Sabek IMokbel M(2017)On Spatial Joins in MapReduceProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3139967(1-10)Online publication date: 7-Nov-2017
https://dl.acm.org/doi/10.1145/3139958.3139967
Nunes SRomani LAvila AColtri PTraina CCordeiro Rde Sousa ETraina ASchwabe DAlmeida VGlaser HBaeza-Yates RMoon S(2013)Analysis of large scale climate dataProceedings of the 22nd International Conference on World Wide Web10.1145/2487788.2487986(517-526)Online publication date: 13-May-2013
https://dl.acm.org/doi/10.1145/2487788.2487986
Cheng CSong XZhou C(2013)Generic cumulative annular bucket histogram for spatial selectivity estimation of spatial database management systemInternational Journal of Geographical Information Science10.1080/13658816.2012.69801727:2(339-362)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1080/13658816.2012.698017
Mamoulis N(2011)Spatial Data ManagementSynthesis Lectures on Data Management10.2200/S00394ED1V01Y201111DTM0213:6(1-149)Online publication date: 30-Nov-2011
https://doi.org/10.2200/S00394ED1V01Y201111DTM021
Ye Wu Wei Xiong Ning Jing Hongsheng Chen (2011)Spatial distance join query estimation without data access2011 IEEE International Conference on Computer Science and Automation Engineering10.1109/CSAE.2011.5952459(228-232)Online publication date: Jun-2011
https://doi.org/10.1109/CSAE.2011.5952459
Bae WAlkobaisi SLeutenegger S(2010)IRSJGeoinformatica10.1007/s10707-009-0089-014:4(507-543)Online publication date: 1-Oct-2010
https://dl.acm.org/doi/10.1007/s10707-009-0089-0
Liu HFrishkoff GFrank RDou D(2010)Ontology-Based mining of brainwavesProceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II10.1007/978-3-642-13672-6_5(43-54)Online publication date: 21-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13672-6_5
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

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

Media

Figures

Other

Tables

View Table of Contents