Overcoming the scalability limitations of parallel star schema data warehouses
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Providing timely results with an elastic parallel DW
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
| Hi-index | 0.00 |
Much has been said about processing efficiently data in parallel database servers, and some data warehouse applications must process in the order of tens to hundreds of Gigabytes efficiently. Yet, there is no effective approach targeted at using non-dedicated low-cost platforms efficiently in this context. Imagine taking together 10 or 1000 commodity PCs and setting-up a data crunching platform for large database-resident data with acceptable performance. There are significant inter-related data layout and processing challenges when the computational, storage and network hardware are heterogeneous and slow. We propose how to place, replicate and load-balance the data efficiently in this context. This work innovates in several respects: being practically as fast as full-mirroring without its overhead, exploring schema, chunk-wise placement, replication and load-balanced processing to be faster and more flexible than previous efforts. Our findings are complemented by an evaluation using TPC-H performance benchmark queries.