Abstract:
Multi-Feature Cube (MF-Cube) query is a complex-data-mining
query based on data cubes, which computes the dependent complex aggregates
at multiple granularities. Existing computations designed for simple data cube
queries can be used to compute distributive and algebraic MF-Cubes queries. In
this paper we propose an efficient computation of holistic MF-Cubes queries.
This method computes. -Kolistic MF-Cubes with PDAP (part Distributive
Aggregate Property). The efficiency is gained by using dynamic subset data
selection strategy (Iceberg query technique) to reduce the size of materialized
data cube. Also for efficiency, this approach adopts the chunk-based caehing
technique to reuse the output of previous queries. We experimentally evaluate
our algorithm using synthetic and real-world datasets, and demonstrate that our
approach delivers up to about twice the performance of traditional
computations.
