The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Segment indexes: dynamic indexing techniques for multi-dimensional interval data
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Comparison of access methods for time-evolving data
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
An Implementation and Performance Analysis of Spatial Data Access Methods
Proceedings of the Fifth International Conference on Data Engineering
Generalized Search Trees for Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
New Linear Node Splitting Algorithm for R-trees
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Performance Analysis of R*-Trees with Arbitrary Node Extents
IEEE Transactions on Knowledge and Data Engineering
A new enhancement to the R-tree node splitting
Journal of Information Science
Organization and maintenance of large ordered indices
SIGFIDET '70 Proceedings of the 1970 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control
Indexing non-uniform spatial data
IDEAS'97 Proceedings of the 1997 international conference on International database engineering and applications symposium
Multi Small Index (MSI): A spatial indexing structure
Journal of Information Science
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A storing of spatial data and processing of spatial queries are important tasks for modern data-bases. The execution efficiency of spatial query depends on underlying index structure. R-tree is a well-known spatial index structure. Currently there exist various versions of R-tree, and one of the most common variations between them is node splitting algorithm. The problem of node splitting in one-dimensional R-tree may seem to be too trivial to be considered separately. One-dimensional intervals can be split on the base of their sorting. Some of the node splitting algorithms for R-tree with two or more dimensions comprise one-dimensional split as their part. However, under detailed consideration, existing algorithms for one-dimensional split do not perform ideally in some complicated cases. This paper introduces a novel one-dimensional node splitting algorithm based on two sortings that can handle such complicated cases better. Also this paper introduces node splitting algorithm for R-tree with two or more dimensions that is based on the one-dimensional algorithm mentioned above. The tests show significantly better behavior of the proposed algorithms in the case of highly overlapping data.