An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Caching multidimensional queries using chunks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Iceberg-cube computation with PC clusters
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Complex Aggregation at Multiple Granularities
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Multiple Query Optimization by Cache-Aware Middleware Using Query Teamwork
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Mining Constrained Gradients in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Divide-and-Approximate: A Novel Constraint Push Strategy for Iceberg Cube Mining
IEEE Transactions on Knowledge and Data Engineering
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This paper proposes a computation method for holistic multi-feature cube (MF-Cube) queries based on the characteristics of MF-Cubes. Three simple yet efficient strategies are designed to optimize the dependent complex aggregate at multiple granularities for a complex data-mining query within data cubes. One strategy is the computation of Holistic MF-Cube queries using the PDAP (Part Distributive Aggregate Property). More efficiency is gained by another strategy, that of dynamic subset data selection (the iceberg query technique), which reduces the size of the materialized data cubes. To extend this efficiency further, the second approach may adopt the chunk-based caching technique that reuses the output of previous queries. By combining these three strategies, we design an algorithm called the PDIC (Part Distributive Iceberg Chunk). We experimentally evaluate this algorithm using synthetic and real-world datasets and demonstrate that our approach delivers up to approximately twice the performance efficiency of traditional computation methods.