A new enhancement to the R-tree node splitting

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 unde...

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Bibliographic Details
Published in:Journal of information science Vol. 36; no. 1; pp. 3 - 18
Main Authors: Al-Badarneh, Amer F., Yaseen, Qussai, Hmeidi, Ismail
Format: Journal Article
Language:English
Published: London, England SAGE Publications 01.02.2010
Sage Publications
Bowker-Saur Ltd
Subjects:
Algorithms
Assignment
Content analysis
Exact sciences and technology
Experiments
Indexes
Indexing. Classification. Abstracting
Indexing. Classification. Abstracting. Syntheses
Information and communication sciences
Information and document structure and analysis
Information processing and retrieval
Information science. Documentation
Online data bases
Online information storage and retrieval
Queries
Query formulation
Rectangles
Rule ordering
Sciences and techniques of general use
Spatial data
Splitting
node splitting
spatial database
R-tree
spatial indexing
Database query
Spatial indexation
R tree
Node method
Spatial database
ISSN:0165-5515, 1741-6485
Online Access:Get full text
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Summary: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.
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ISSN:0165-5515
1741-6485
DOI:10.1177/0165551509340360