Name
Abstract | ||
|---|---|---|
Analytical queries defined on data warehouses are complex and use several join operations that are very costly, especially when run on very large data volumes. To improve response times, data warehouse administrators casually use indexing techniques. This task is nevertheless complex and fastidious. In this paper, we present an automatic, dynamic index selection method for data warehouses that is based on incremental frequent itemset mining from a given query workload. The main advantage of this approach is that it helps update the set of selected indexes when workload evolves instead of recreating it from scratch. Preliminary experimental results illustrate the efficiency of this approach, both in terms of performance enhancement and overhead. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IIT.2007.4430394 | Computing Research Repository |
Keywords | DocType | Volume |
data mining,data warehouses,very large databases,dynamic index selection,incremental frequent itemset mining,data warehouse,indexation | Journal | abs/0809.1 |
ISSN | ISBN | Citations |
4th International Conference on Innovations in Information
Technology (Innovations 07), Dubai : \'Emirats arabes unis (2006) | 978-1-4244-1841-1 | 8 |
PageRank | References | Authors |
0.47 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stéphane Azefack | 1 | 8 | 0.47 |
| Kamel Aouiche | 2 | 233 | 13.32 |
| Jérôme Darmont | 3 | 382 | 50.93 |