Array Databases

Living reference work entry



Array (also called raster or grid): a collection of data items sharing the same data type where each item has a coordinate associated which sits at grid points in a rectangular, axis-parallel subset of the Euclidean space Zd for some d > 0 (same as arrays in programming languages).

Array database system: a database system with modeling and query support for multidimensional arrays.

Array query language: a query language allowing declarative retrieval on multidimensional arrays.

Historical Background

Traditionally, all data not tractable with relational tables have been considered “unstructured”; this has long included multidimensional (“n-D”) arrays although these have a very regular structure. Arrays form an important, widespread information structure appearing in virtually all domains and effectively make up for a large part of today’s “Big Data” as spatiotemporal sensor, image, simulation, and statistics data in science, engineering, business,...


Array Database Rasdaman Web Coverage Processing Service (WCPS) Array Query Language Array Element Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Baumann P. On the management of multidimensional discrete data. VLDB J. 1994;4(3):401–44. Special Issue on Spatial Database Systems.CrossRefGoogle Scholar
  2. 2.
    Baumann P. A database array algebra for spatio-temporal data and beyond. Proceedings of the NGITS’99. Lecture notes in computer science, vol. 1649. 1999. p. 76–93.Google Scholar
  3. 3.
    Baumann P. The OGC web coverage processing service (WCPS) standard. GeoInformatica. 2010;14(4):447–79.CrossRefGoogle Scholar
  4. 4.
    Baumann P. OGC web coverage processing service (WCPS) language interface standard. OGC document 08-068r2; 2010a.Google Scholar
  5. 5.
    Baumann P, Feyzabadi S, Jucovschi C. Putting pixels in place: a storage layout language for scientific data. Proceedings of the IEEE ICDM Workshop on Spatial and Spatiotemporal Data Mining (SSTDM), 14 Dec 2010, Sydney, Australia. 2010b. p. 194–201.Google Scholar
  6. 6.
    Baumann P, Stamerjohanns H. Benchmarking large arrays in databases. Proceedings of the Workshop on Big Data Benchmarking (WBDB), 17–18 Dec 2012, Pune, India. Springer LNCS 8163. 2012. p. 94–102.Google Scholar
  7. 7.
    Buck J, Watkins N, LeFevre J, Ioannidou K, Maltzahn C, Polyzotis N, Brandt SA. SciHadoop: array-based query processing in Hadoop. Proceedings of the High Performance Computing, Networking, Storage and Analysis Super Computing, Seattle, USA. 2011. p. 66:1–66:11.Google Scholar
  8. 8.
    Cheng Y, Rusu F. Astronomical data processing in EXTASCID. In: Szalay A, Budavari T, Balazinska M, Meliou A, Sacan A editors. Proceedings of the 25th International Conference on Scientific and Statistical Database Management (SSDBM). New York: ACM. Article 47. doi: 10.1145/2484838.2484875.
  9. 9.
    Cheng Y, Rusu F. Formal representation of the SS-DB benchmark and experimental evaluation in EXTASCID. Distrib Parallel Databases. 2013;33:277. doi: 10.1007/s10619-014-7149-7.CrossRefGoogle Scholar
  10. 10.
    Chock M, Cardenas A, Klinger A. Database structure and manipulation capabilities of a picture database management system (PICDMS). IEEE ToPAMI. 1984;6(4):484–92.CrossRefGoogle Scholar
  11. 11.
    Dehmel A. A compression engine for multidimensional array database systems. PhD thesis, TU München; 2002.Google Scholar
  12. 12.
    Dumitru A, Merticariu V, Baumann P. Exploring cloud opportunities from an array database perspective. Proceedings of ACM SIGMOD Workshop on Data Analytics in the Cloud (DanaC), 22–27 June 2014, Snowbird, USA. 2014.Google Scholar
  13. 13.
    EarthServer: The EarthServer Initiative. Seen 12 Apr 2017.
  14. 14.
    Furtado P, Baumann P. Storage of multidimensional arrays based on arbitrary tiling. Proceedings of the International Conference on Data Engineering (ICDE), 23–26 Mar 1999, Sydney, Australia. 1999. p. 328–36.Google Scholar
  15. 15.
    Hahn K, Reiner B. Parallel query support for multidimensional data: inter-object parallelism. Proceedings of the DEXA, 2002, Aix en Provence, France. 2002.Google Scholar
  16. 16.
    Libkin L, Machlin R, Wong L. A query language for multidimensional arrays: design, implementation and optimization techniques. Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’ 96). 1996. p. 228–39.Google Scholar
  17. 17.
    Machlin R. Index-based multidimensional array queries: safety and equivalence. Proceedings of the ACM PODS, June 2007, Beijing, China. 2007.Google Scholar
  18. 18.
    Marathe A, Salem K. A language for manipulating arrays. Proceedings of the VLDB’97, Aug 1997, 1997. p. 46–55.Google Scholar
  19. 19.
    Melton J, Baumann P, Misev D. ISO/IEC 9075–15 SQL MDA (multi-dimensional arrays).Google Scholar
  20. 20.
    Mennis J, Viger R, Tomlin CD. Cubic map algebra functions for spatio-temporal analysis. Cartogr Geogr Inf Sci. 2005;32(1):17–32.CrossRefGoogle Scholar
  21. 21.
    Merticariu G, Misev D, Baumann P. Measuring storage access performance in array databases. Proceedings of the 7th Workshop on Big Data Benchmarking (WBDB), 14–15 Dec 2016, New Delhi, India. 2016.Google Scholar
  22. 22.
    Misev D, Baumann P. Extending the SQL array concept to support scientific analytics. Proceedings of the Scientific and Statistical Database Management (SSDBM); 2014 June 30–July 2, Aalborg, Denmark. 2014. p. 10:1–10:11.Google Scholar
  23. 23.
    N.n.: ISO/IEC 19139 XML schema, Seen 29 July 2014.
  24. 24.
    N.n.: ISO/IEC 9075–1 SQL Foundation.Google Scholar
  25. 25.
    N.n.: Multipurpose internet mail extensions (MIME) part one: format of internet message bodies, Seen 12 Apr 2017.
  26. 26.
    Pisarev A, Poustelnikova E, Samsonova M, Baumann P. Mooshka: a system for the management of multidimensional gene expression data in situ. Inf Syst. 2003;28(4):269–85.CrossRefzbMATHGoogle Scholar
  27. 27.
    PostGIS: PostGIS Raster manual. Seen 29 July 2014.Google Scholar
  28. 28.
    RDA: Array Database Assessment Working Group. Seen 12 Apr 2017.
  29. 29.
    Reiner B, Hahn K. Hierarchical storage support and management for large-scale multidimensional array database management systems. Proceedings of the DEXA, 2002, Aix en Provence, France. 2002.Google Scholar
  30. 30.
    Sarawagi S, Stonebraker M. Efficient organization of large multidimensional arrays. Proceedings of the International Conference on Data Engineering ICDE, 1994, Houston, USA. 1994. p. 328–36.Google Scholar
  31. 31.
    Soroush E, Balazinska M, Wang D. ArrayStore: a storage manager for complex parallel array processing. Proceedings of the ACM SIGMOD, Athens, Greece. 2011. p. 253–64.Google Scholar
  32. 32.
    Stonebraker M, Brown P, Poliakov A, Raman S. The architecture of SciDB. Proceedings of the 23rd International Conference on Scientific and Statistical Database Management, SSDBM’11, 2011. Berlin, Heidelberg: Springer-Verlag; 2011. p. 1–16.Google Scholar
  33. 33.
    Teradata: User-Defined Data Type, ARRAY Data Type, and VARRAY Data Type Limits. Seen 29 July 2014.Google Scholar
  34. 34.
    XLDB: Science Benchmark. Seen 12 Apr 2017.
  35. 35.
    Zhang Y, Kersten M L, Ivanova M, Nes, N. SciQL, bridging the gap between science and relational DBMS. In: Desai BC, Cruz IF, Bernardino J, editors. Proceedings of the 15th Symposium on International Database Engineering and Applications (IDEAS), 27–29 Sept 2011, Lisbon, Portugal. 2011. p. 124–33.Google Scholar

Authors and Affiliations

  1. 1.Jacobs UniversityBremenGermany

Section editors and affiliations

  • Amarnath Gupta
    • 1
  1. 1.San Diego Supercomputer CenterUniv. of California San DiegoLa JollaUSA