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ACM Computing Surveys (CSUR)
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SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
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Data & Knowledge Engineering
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ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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ICDE '00 Proceedings of the 16th International Conference on Data Engineering
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Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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Indexing the fully evolvement of spatiotemporal objects
WSEAS Transactions on Information Science and Applications
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while experience shows that contemporary multi-dimensional access methods perform poorly in high-dimensional spaces, little is known about the underlying causes of this important problem. One of the factors that has a profound effect on the performance of a multi-dimensional structure in high-dimensional situations is its space partitioning strategy. This paper investigates the partitioning strategies of KDB-trees, the Pyramid Technique, and a new point access method called the Θs Technique. The paper reveals important dimensionality problems associated with these strategies and shows how each strategy affects the retrieval performance across a range of spaces with varying dimensionalities. The Pyramid Technique, which is frequently regarded as the state-of-the-art access method for high-dimensional data, suffers from numerous problems that become particularly severe with highly skewed data in heavily, sparse spaces. Although the partitioning strategy of KDB-trees incurs several problems in high-dimensional spaces, it exhibits a remarkable adaptability to the changing data distributions. However, the experimental evidence gathered on both simulated and real data sets shows that the Θs Technique generally outperforms the other two schemes in high-dimensional spaces, usually by a significant margin.