Abstract An Adaptive View Element Framework for Multi-dimensional Data Management

We present an adaptive wavelet view element framework for managing different types of multi-dimensional data in stor-age and retrieval applications. We consider the problems of multi-dimensional data compression, multi-resolution sub-region access, selective materialization, progressive retrieval and similarity searching. The framework uses wavelets to partition the multi-dimensional data into view elements that form the building blocks for synthesizing views of the data. The view elements are organized and managed using dif-ferent view element graphs. The graphs are used to guide cost-based view element selection algorithms for optimizing compression, access, retrieval and search performance. We present the adaptive wavelet view element framework and describe its application in managing multi-dimensional data such as 1-D time series data, 2-D images, video se-quences, and mcblti-dimensional data cubes. We present ex-perimental results that demonstrate that the adaptive wavelet view element framework improves performance of compress-ing, accessing, and retrieving multi-dimensional data com-pared to non-adaptive methods