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 storage and retrieval applications. We consider the problems of multi-dimensional data compression, multi-resolution subregion 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 different 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 sequences, and multi-dimensional data cubes. We present experimental results that demonstrate that the adaptive wavelet view element framework improves performance of compressing, accessing, and retrieving multi-dimensional data compared to non-adaptive methods.