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
Towards an analysis of range query performance in spatial data structures
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Enclosing a set of objects by two minimum area rectangles
Journal of Algorithms
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
ACM Computing Surveys (CSUR)
Computational Geometry in C
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Optimization Issues in R-tree Construction (Extended Abstract)
IGIS '94 Proceedings of the International Workshop on Advanced Information Systems: Geographic Information Systems
On Optimal Node Splitting for R-trees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Generalized Search Trees for Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
GBI: A Generalized R-Tree Bulk-Insertion Strategy
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Indexed-based density biased sampling for clustering applications
Data & Knowledge Engineering
Main-memory operation buffering for efficient R-tree update
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Trees or grids?: indexing moving objects in main memory
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A new enhancement to the R-tree node splitting
Journal of Information Science
Spatial indexing for massively update intensive applications
Information Sciences: an International Journal
Elastic and effective spatio-temporal query processing scheme on Hadoop
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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Spatial indexing is a well researched field that benefited computer science with many outstanding results. Our effort in this paper can be seen as revisiting some outstanding contributions to spatial indexing, questioning some paradigms, and designing an access method with globally improved performance characteristics. In particular, we argue that dynamic R-tree construction is a typical clustering problem which can be addressed by incorporating existing clustering algorithms. As a working example, we adopt the well-known k-means algorithm. Further, we study the effect of relaxing the "two-way" split procedure and propose a "multi-way" split, which inherently is supported by clustering techniques. We compare our clustering approach to two prominent examples of spatial access methods, the R- and the R*-tree.