Wolap: wavelet-based on-line analytical processing

Wavelet Transform has emerged as an elegant tool for online analytical queries. Most of the methods using wavelets, however, share the disadvantage of providing only data-dependant approximate answers by compressing the data. On the contrary, we propose a wavelet-based query processing technique, WOLAP, which does not require compressing the data. Instead, we employ wavelet transform to compact incoming queries rather than the underlying data. The intuition here is that queries are well-formed with repetitive patterns that can be exploited by wavelets for a more effective compression, leading to efficient query performance. WOLAP extends the set of ad-hoc analytical queries to include the entire family of range polynomial aggregate queries as well as the complex class of range group-by queries. In addition, leveraging from the multi-resolution property of wavelets, WOLAP supports progressive and approximate query processing in case of time or space limitation. Toward realizing the practical use of WOLAP, we provide a framework to efficiently maintain large multidimensional wavelet-transformed data. In particular, by introducing two novel operations which work directly in the wavelet domain, we allow WOLAP to transform, reconstruct, store, update, and append data in an I/O efficient manner. By developing a real system and conducting extensive sets of experiments with several real-world datasets, we have verified the effectiveness of WOLAP in practice.