Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 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
A Shifting Algorithm for Min-Max Tree Partitioning
Journal of the ACM (JACM)
Introduction to algorithms
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
High Performance OLAP and Data Mining on Parallel Computers
Data Mining and Knowledge Discovery
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Storage Estimation for Multidimensional Aggregates in the Presence of Hierarchies
VLDB '96 Proceedings of the 22th 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
A Parallel Scalable Infrastructure for OLAP and Data Mining
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
Attribute value reordering for efficient hybrid OLAP
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Attribute value reordering for efficient hybrid OLAP
Information Sciences: an International Journal
Discovering OLAP dimensions in semi-structured data
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
Discovering dynamic classification hierarchies in OLAP dimensions
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
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We study the applicability of coarse grained parallel computing model (CGM) to on-line analytical processing (OLAP) for data mining. We present a general framework for the CGM which allows for the efficient parallelization of existing data cube construction algorithms for OLAP. Experimental data indicate that our approach yield optimal speedup, even when run on a simple processor cluster connected via a standard switch.