Abstract
Efficiently accessing multidimensional data is a challenge for building modern database applications that involve many folds of data such as temporal, spatial, data warehousing, bio-informatics, etc. This problem stems from the fact that multidimensional data have no given order that preserves proximity. The majority of the existing solutions to this problem cannot be easily integrated into the current relational database systems since they require modifications to the kernel. A prominent class of methods that can use existing access structures are ‘space filling curves’. In this study, we describe a method that is also based on the space filling curve approach, but in contrast to earlier methods, it connects regions of various sizes rather than points in multidimensional space. Our approach allows an efficient transformation of interval queries into regions of data that results in significant improvements when accessing the data. A detailed empirical study demonstrates that the proposed method outperforms the best available off-the-shelf methods for accessing multidimensional data.
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Terry, J., Stantic, B., Terenziani, P., Sattar, A. (2011). Variable Granularity Space Filling Curve for Indexing Multidimensional Data. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds) Advances in Databases and Information Systems. ADBIS 2011. Lecture Notes in Computer Science, vol 6909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23737-9_9
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DOI: https://doi.org/10.1007/978-3-642-23737-9_9
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