A query interaction system generates questioning dimensions to guide users toward precise search intent.
Dynamic aggregate generation creates pre-computed tables from virtual multidimensional models to reduce query response time.
Performance-cost models autonomously allocate columns to row or column structures, reducing primary storage footprint while maintaining query execution speed.
Segmenting cache pools isolates scan operations from OLTP workloads, preventing large datasets from displacing frequently accessed transactional data.
An optimization system segments multi-dimensional data and columns based on identified query patterns to improve storage efficiency.
A distributed dwarf cube uses MapReduce to partition data blocks and sort dimensions by cardinality for efficient processing.