Skip to main content
SpringerLink
Log in

Query Processing: Joins

  • Living reference work entry
  • First Online:
Encyclopedia of Big Data Technologies

  • 447 Accesses

Synonyms

Parallel spatial joins; Spatial joins; Spatial joins in clusters

Definition

Given two collections R and S of spatial objects and a spatial predicate θ, the spatial join computes the pairs of objects (r, s) such that r ∈ R, s ∈ S, and r θ s. Common spatial predicates are intersect, inside, and contains. For example, consider a GIS application, where R is a collection of rivers and S is a collection of road segments. The spatial join of R and S finds the pairs of rivers and roads that intersect. If the spatial objects are points, the most common type of spatial join is the distance join, where θ is “within distance 𝜖 from”; here, 𝜖 is a given threshold. For example, assuming that R is the set of hotels on a city map and S is the set of restaurants on the same map, the distance join finds pairs of hotels and restaurants that are sufficiently close to each other (e.g., 𝜖 = 100 m).

Overview

For objects with spatial extent, a common practice is to evaluate spatial joins (and...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Aji A, Wang F, Saltz JH (2012) Towards building a high performance spatial query system for large scale medical imaging data. In: SIGSPATIAL/GIS, pp 309–318

    Google Scholar 

  • Aji A, Wang F, Vo H, Lee R, Liu Q, Zhang X, Saltz JH (2013) Hadoop-GIS: a high performance spatial data warehousing system over mapreduce. PVLDB 6(11):1009–1020

    Google Scholar 

  • Arge L, Procopiuc O, Ramaswamy S, Suel T, Vitter JS (1998) Scalable sweeping-based spatial join. In: VLDB, pp 570–581

    Google Scholar 

  • Brinkhoff T, Kriegel HP, Seeger B (1993) Efficient processing of spatial joins using r-trees. In: SIGMOD conference, pp 237–246

    Google Scholar 

  • Brinkhoff T, Kriegel H, Seeger B (1996) Parallel processing of spatial joins using R-trees. In: ICDE, pp 258–265

    Google Scholar 

  • Dittrich J, Seeger B (2000) Data redundancy and duplicate detection in spatial join processing. In: ICDE, pp 535–546

    Google Scholar 

  • Eldawy A, Mokbel MF (2015) SpatialHadoop: a mapreduce framework for spatial data. In: ICDE, pp 1352–1363

    Google Scholar 

  • Güting RH (1994) An introduction to spatial database systems. VLDB J 3(4):357–399

    Article  Google Scholar 

  • Jacox EH, Samet H (2007) Spatial join techniques. ACM Trans Database Syst 32(1):7

    Article  Google Scholar 

  • Koudas N, Sevcik KC (1997) Size separation spatial join. In: SIGMOD conference, pp 324–335

    Google Scholar 

  • Leutenegger ST, Edgington JM, López MA (1997) STR: a simple and efficient algorithm for r-tree packing. In: ICDE, pp 497–506

    Google Scholar 

  • Lo ML, Ravishankar CV (1996) Spatial hash-joins. In: SIGMOD conference, pp 247–258

    Google Scholar 

  • Lo ML, Ravishankar CV (1998) The design and implementation of seeded trees: an efficient method for spatial joins. IEEE Trans Knowl Data Eng 10(1):136–152

    Article  Google Scholar 

  • Mamoulis N (2009) Spatial join. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, New York/London, pp 2707–2714

    Google Scholar 

  • Mamoulis N, Papadias D (2001) Multiway spatial joins. ACM Trans Database Syst 26(4):424–475

    Article  MATH  Google Scholar 

  • Mamoulis N, Papadias D (2003) Slot index spatial join. IEEE Trans Knowl Data Eng 15(1):211–231

    Article  Google Scholar 

  • McKenney M, Frye R, Dellamano M, Anderson K, Harris J (2017) Multi-core parallelism for plane sweep algorithms as a foundation for GIS operations. GeoInformatica 21(1):151–174

    Article  Google Scholar 

  • Nobari S, Tauheed F, Heinis T, Karras P, Bressan S, Ailamaki A (2013) TOUCH: in-memory spatial join by hierarchical data-oriented partitioning. In: SIGMOD conference, pp 701–712

    Google Scholar 

  • Nobari S, Qu Q, Jensen CS (2017) In-memory spatial join: the data matters! In: EDBT, pp 462–465

    Google Scholar 

  • Patel JM, DeWitt DJ (1996) Partition based spatial-merge join. In: SIGMOD conference, pp 259–270

    Google Scholar 

  • Patel JM, DeWitt DJ (2000) Clone join and shadow join: two parallel spatial join algorithms. In: ACM-GIS, pp 54–61

    Google Scholar 

  • Preparata FP, Shamos MI (1985) Computational geometry – an introduction. Springer, New York

    Book  MATH  Google Scholar 

  • Ray S, Simion B, Brown AD, Johnson R (2014) Skew-resistant parallel in-memory spatial join. In: SSDBM, pp 6:1–6:12

    Google Scholar 

  • Samet H (1990) The design and analysis of spatial data structures. Addison-Wesley, Reading

    Google Scholar 

  • Szalay AS, Kunszt PZ, Thakar A, Gray J, Slutz DR, Brunner RJ (2000) Designing and mining multi-terabyte astronomy archives: the sloan digital sky survey. In: SIGMOD conference, pp 451–462

    Google Scholar 

  • Xie D, Li F, Yao B, Li G, Chen Z, Zhou L, Guo M (2016) Simba: spatial in-memory big data analysis. In: SIGSPATIAL/GIS, pp 86:1–86:4

    Google Scholar 

  • You S, Zhang J, Gruenwald L (2015) Large-scale spatial join query processing in cloud. In: CloudDB, ICDE workshops, pp 34–41

    Google Scholar 

  • Yu J, Wu J, Sarwat M (2015) GeoSpark: a cluster computing framework for processing large-scale spatial data. In: SIGSPATIAL/GIS, pp 70:1–70:4

    Google Scholar 

  • Zhang J, Mamoulis N, Papadias D, Tao Y (2004) All-nearest-neighbors queries in spatial databases. In: SSDBM, pp 297–306

    Google Scholar 

  • Zhang S, Han J, Liu Z, Wang K, Xu Z (2009) SJMR: parallelizing spatial join with mapreduce on clusters. In: CLUSTER, pp 1–8

    Google Scholar 

  • Zhou X, Abel DJ, Truffet D (1997) Data partitioning for parallel spatial join processing. In: SSD, pp 178–196

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

Authors
  1. Nikos Mamoulis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikos Mamoulis .

Editor information

Editors and Affiliations

  1. School of Comp. Sci. and Engineering, University of New South Wales School of Comp. Sci. and Engineering, Eveleigh, New South Wales, Australia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. Data Science Research Institute, Swinburne University of Technology, Melbourne, Australia

     & Timos Sellis & Aamir Cheema

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Mamoulis, N. (2018). Query Processing: Joins. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_219-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63962-8_219-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation