Efficiently accessing multidimensiol 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 multidimensiol data have no given order that preserves proximity. The majority of the existing solutions to this problem cannot be easily integrated into the current relatiol 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 multidimensiol 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-theshelf methods for accessing multidimensiol data.
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