Amit Shukla

TL;DR: This dissertation describes techniques for speeding up Online Analytical Processing or OLAP queries, and presents an empirical study of PBS that demonstrates that PBS picks a surprisingly good set of aggregates even when the conditions do not hold, and proposes algorithms that perform significantly better than previously proposed algorithms for multicube workloads.

TL;DR: Chunk-based caching allows fine granularity caching, and allows queries to partially reuse the results of previous queries with which they overlap, and a new organization for relational tables, which is called a “chunked file” is proposed.

TL;DR: Three strategies for estimating the storage blowup that will result from a proposed set of precomputations without actually computing them are proposed: one based on sampling, onebased on mathematical approximation, and one based upon probabilistic counting.

TL;DR: The initial performance results suggest that the exploitation of common subtask evaluation and global optimization can yield substantial performance improvements when relational database systems are used as data sources for multidimensional analysis.

TL;DR: A detailed cost model for predicting join algorithm performance is developed, and the model is used to develop cost formulas for the major ad hoc join methods found in the relational database literature.