Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Dynamic maintenance of multidimensional range data partitioning for parallel data processing
Proceedings of the 1st ACM international workshop on Data warehousing and OLAP
A dynamic load balancing strategy for parallel datacube computation
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Range queries in dynamic OLAP data cubes
Data & Knowledge Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Hierarchical Prefix Cubes for Range-Sum Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Dynamic Update Cube for Range-sum Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Aggregate-Based Query Processing in a Parallel Data Warehouse Server
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
A Cluster Architecture for Parallel Data Warehousing
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
An Infrastructure for Scalable Parallel Multidimensional Analysis
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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I/O parallelism is considered to be a promising approach to achieving high performance in parallel data warehousing systems where huge amounts of data and complex analytical queries have to be processed. This paper proposes a parallel secondary data cube storage structure (PHC for short) to efficiently support the processing of range sum queries and dynamic updates on data cube using parallel computing systems. Based on PHC, two parallel algorithms for processing range sum queries and updates are proposed also. Both the algorithms have the same time complexity, O(logd n/P). The analytical and experimental results show that PHC and the parallel algorithms have high performance and achieve optimum speedup.