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The n-dimensional k-vector and its application to orthogonal range searching
David Arnas1, Carl Leake2, Daniele Mortari2
1Universidad de Zaragoza, Valentin Carderera 4, Huesca 22003, Spain.
Applied Mathematics and Computation
|May 27, 2020
Summary
This study introduces the n-dimensional k-vector, an efficient algorithm for orthogonal range searching in multidimensional databases. It optimizes dimension search order for improved performance in static datasets.
Area of Science:
- Computer Science
- Database Systems
- Algorithms
Background:
- Orthogonal range searching is crucial for querying multidimensional static databases.
- Existing methods face challenges in optimizing search across multiple dimensions.
Purpose of the Study:
- To define and study the n-dimensional k-vector algorithm.
- To enhance orthogonal range searching efficiency in multidimensional databases.
Main Methods:
- The algorithm determines optimal dimension search order using a 1D k-vector technique.
- It employs a modified projection method for the search.
- Predictive analysis guides the approach for each dimension.
Main Results:
- The n-dimensional k-vector algorithm achieves a worst-case complexity of O(nd(k/n)^(2/d)).
- Performance analysis demonstrates its effectiveness in multidimensional range searching.
Conclusions:
- The n-dimensional k-vector provides an effective solution for orthogonal range searching.
- The methodology offers a novel approach to optimizing multidimensional database queries.
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