Parallel Multi-Dimensional ROLAP Indexing

Authors:
Frank Dehne;Todd Eavis;Andrew Rau-Chaplin
Affiliations:
-;-;-
Venue:
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Year:
2003

Citing 0
Cited 8

The cgmCUBE project: Optimizing parallel data cube generation for ROLAP

Distributed and Parallel Databases
Compact Hilbert indices: Space-filling curves for domains with unequal side lengths

Information Processing Letters
Cooperative caching for grid-enabled OLAP

International Journal of Grid and Utility Computing
Architecture of Parallel Spatial Data Warehouse: Balancing Algorithm and Resumption of Data Extraction

Proceedings of the 2005 conference on Software Engineering: Evolution and Emerging Technologies
Materialized aR-Tree in Distributed Spatial Data Warehouse

Intelligent Data Analysis - Analysis of Symbolic and Spatial Data
Exploring graphics processing units as parallel coprocessors for online aggregation

DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
Online balancing of ar-tree indexed distributed spatial data warehouse

PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
Adaptive Tuple differential coding

DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the query performance issuefor Relational OLAP (ROLAP) datacubes. Wepresent a distributed multi-dimensional ROLAP indexingscheme which is practical to implement, requiresonly a small communication volume, and is fullyadapted to distributed disks. Our solution is efficientfor spatial searches in high dimensions and scalable interms of data sizes, dimensions, and number of processors.Our method is also incrementally maintainable.Using "surrogate" group-bys, it allows for the efficientprocessing of arbitrary OLAP queries on partial cubes,where not all of the group-bys have been materialized.Our experiments show that the ROLAP advantageof better scalability, in comparison to MOLAP, can bemaintained while providing, at the same time, a fastand flexible index for OLAP queries.