Development of an index structure for spatial data
Multidimensional data in data bases and algorithms
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In the work, a new index structure for multidimensional data in the databases is developed, it is called HPK -hyperplane tree. It was a surprise. In the beginning, the author deals with the known predecessor methods and algorithms in order to show what is already done for the science, and to explore newer unknown ways for her work. In the first chapter, the most successful space and data partitioning methods will be discussed, and they serve as a comparison criterion for the next and for the new HPK-hyperplane tree. In the second chapter, the generalized hyperplane trees and vantage point tree...
In the work, a new index structure for multidimensional data in the databases is developed, it is called HPK -hyperplane tree. It was a surprise. In the beginning, the author deals with the known predecessor methods and algorithms in order to show what is already done for the science, and to explore newer unknown ways for her work. In the first chapter, the most successful space and data partitioning methods will be discussed, and they serve as a comparison criterion for the next and for the new HPK-hyperplane tree. In the second chapter, the generalized hyperplane trees and vantage point trees are considered. In the third chapter, other metric trees are mentioned. In the last chapter, the new index structure is presented. This is both: a data- and space partitioning index structure. The HPK-index structure is developed and tested in Java with VRML 3D-visualisations, some pictures are encluded. Enjoy reading this book.
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