Multidimensional access methods

Article Properties
  • Language
    English
  • Publication Date
    1998/06/01
  • Indian UGC (Journal)
  • Refrences
    193
  • Citations
    395
  • Volker Gaede Imperial College, London, UK
  • Oliver Günther Humboldt-Univ. zu Berlin, Berlin, Germany
Abstract
Cite
Gaede, Volker, and Oliver Günther. “Multidimensional Access Methods”. ACM Computing Surveys, vol. 30, no. 2, 1998, pp. 170-31, https://doi.org/10.1145/280277.280279.
Gaede, V., & Günther, O. (1998). Multidimensional access methods. ACM Computing Surveys, 30(2), 170-231. https://doi.org/10.1145/280277.280279
Gaede V, Günther O. Multidimensional access methods. ACM Computing Surveys. 1998;30(2):170-231.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Computer software
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Computer engineering
Computer hardware
Description

Need faster database searches? This paper provides a comprehensive overview of multidimensional access methods, crucial for efficient search operations in both conventional and spatial databases. It focuses on methods designed to support point queries (finding objects containing a specific point) and region queries (finding objects overlapping a given region). The review categorizes and explains two main classes of access methods: point access methods (for searching sets of points) and spatial access methods (for handling extended objects like rectangles or polyhedra). It also discusses the theoretical underpinnings and experimental results associated with various approaches. By summarizing over a decade of spatial database research, the article offers valuable insights for database designers and researchers. Understanding the trade-offs between different access methods is vital for optimizing database performance, particularly in applications dealing with spatial data or other multidimensional datasets.

As a survey published in ACM Computing Surveys, this article aligns with the journal's aim to provide broad overviews of important areas within computer science. The focus on multidimensional access methods fits with the journal's emphasis on database systems, algorithms, and data structures, offering readers a synthesis of knowledge.

Refrences
Citations
Citations Analysis
The first research to cite this article was titled MOF-tree: A spatial access method to manipulate multiple overlapping features and was published in 1997. The most recent citation comes from a 2024 study titled MOF-tree: A spatial access method to manipulate multiple overlapping features . This article reached its peak citation in 2006 , with 31 citations.It has been cited in 167 different journals, 7% of which are open access. Among related journals, the IEEE Transactions on Knowledge and Data Engineering cited this research the most, with 27 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year