Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A dynamic load balancing strategy for parallel datacube computation
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
PARSIMONY: An infrastructure for parallel multidimensional analysis and data mining
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Iceberg-cube computation with PC clusters
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
High Performance OLAP and Data Mining on Parallel Computers
Data Mining and Knowledge Discovery
Fully Dynamic Partitioning: Handling Data Skew in Parallel Data Cube Computation
Distributed and Parallel Databases
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
Future Generation Computer Systems - Selected papers from CCGRID 2002
Parallel ROLAP Data Cube Construction on Shared-Nothing Multiprocessors
Distributed and Parallel Databases
Communication and Memory Optimal Parallel Data Cube Construction
IEEE Transactions on Parallel and Distributed Systems
The cgmCUBE project: Optimizing parallel data cube generation for ROLAP
Distributed and Parallel Databases
An incremental maintenance scheme of data cubes
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
An extendible array based implementation of relational tables for multi dimensional databases
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
The pre-computation of data cubes is critical for improving the response time of OLAP On-Line Analytical Processing systems. To meet the need for improved performance created by growing data sizes, parallel solutions for data cube construction are becoming increasingly important. This paper presents a new parallel data cube construction scheme based on an extendible multidimensional array, which is dynamically extendible along any dimension without relocating any existing data. The authors have implemented and evaluated their parallel data cube construction methods on shared-memory multiprocessors. Given the performance limit, the methods achieve close to linear speedup with load balance. The authors' experiments also indicate that their parallel methods can be more scalable on higher dimensional data cube construction.