On the Importance of Tuning in Incremental View Maintenance: An Experience Case Study
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
DSD: maintain data cubes more efficiently
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
DSD: Maintain Data Cubes More Efficiently
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
| Hi-index | 0.00 |
We originally set out to explore the performance of a particular algorithm for incremental maintenance of a materialized view in a data warehouse. We chose to use a substantial database schema and population, derived from the TPC-D family of benchmarks, with a population size of around one gigabyte. In the process, we discovered that at this size, unexpected issues with the query and update processing had to be understood and dealt with before the results would be meaningful. We discover that incremental maintenance is feasible over a wide range of update sizes (granularities), and that in all cases a cursor-based version of the algorithm performs the best. This is a report of both the results of the original inquiry and of some of the events encountered.