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SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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ACM Computing Surveys (CSUR)
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Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
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IDEAS '97 Proceedings of the 1997 International Symposium on Database Engineering & Applications
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Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A novel improvement to the R*-tree spatial index using gain/loss metrics
Proceedings of the 12th annual ACM international workshop on Geographic information systems
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
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The R*-tree, as a state-of-the-art spatial index, has already found its way into commercial systems like Oracle. In this paper, we aim at improving query performance of the R*-tree. We focus on five widely used spatial queries: range query, aggregation query, nearest neighbor query, skyline query, and join query. The idea is to store outlier objects in internal tree nodes. The new structure is named the ROtree. Here an outlier is an object which is located far from other objects or has large extent (we consider both point objects and objects with extent). If such objects are stored at higher levels of the tree, the lower-level nodes have smaller minimum bounding rectangles and thus the index performs better. To support the dynamic nature of the index, several structural and algorithmic changes are needed. The paper discusses these changes. In particular, we show how to identify and handle the outlier objects during page overflow/underflow, using gain/loss metrics. Extensive experiments reveal that the ROtree significantly outperforms the R*-tree for all the five queries.