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
Topological relations in the world of minimum bounding rectangles: a study with R-trees
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Branch grafting method for R-tree implementation
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
ACM Computing Surveys (CSUR)
Optimizing storage utilization in R-tree dynamic index structure for spatial databases
Journal of Systems and Software
Multimedia exploratory data analysis for geospatial data mining: the case for augmented seriation
Journal of the American Society for Information Science and Technology
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
Indexing the Solution Space: A New Technique for Nearest Neighbor Search in High-Dimensional Space
IEEE Transactions on Knowledge and Data Engineering
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Revisiting R-Tree Construction Principles
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
New Linear Node Splitting Algorithm for R-trees
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
The Retrieval of Direction Relations using R-trees
DEXA '94 Proceedings of the 5th International Conference on Database and Expert Systems Applications
Information Processing and Management: an International Journal
SaIL: A Library for Efficient Application Integration of Spatial Indices
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
Spatial Databases: Technologies, Techniques and Trends
Spatial Databases: Technologies, Techniques and Trends
Geographic information retrieval in a mobile environment: evaluating the needs of mobile individuals
Journal of Information Science
Retrieval of images by spatial and object similarities
Information Processing and Management: an International Journal
An efficient strategy for storing and searching binary trees in WORM external memory
Journal of Information Science
A new double sorting-based node splitting algorithm for R-tree
Programming and Computing Software
Multi Small Index (MSI): A spatial indexing structure
Journal of Information Science
Corner-based splitting: An improved node splitting algorithm for R-tree
Journal of Information Science
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The performance of spatial queries depends mainly on the underlying index structure used to handle them. R-tree, a well-known spatial index structure, suffers largely from high overlap and high coverage resulting mainly from splitting the overflowed nodes. Assigning the remaining entries to the underflow node in order to meet the R-tree minimum fill constraint (Remaining Entries problem) may induce high overlap or high coverage. This is done without considering the geometric features of the remaining entries and this may cause a very non-optimized expansion of that particular node. This paper presents a solution to the above problem. The proposed solution to this problem distributes rectangles as follows: (1) assign m entries to the first node, which are nearest to the first seed; (2) assign other m entries to the second node, which are nearest to the second seed; (3) assign the remaining entries one by one to the nearest seed. Several experiments on real data, as well as synthetic data, show that the proposed splitting algorithm outperforms the efficient version of the original R-tree in terms of query performance.