Name
Abstract | ||
|---|---|---|
When utilising multidimensional OLAP (On-Line Analytic Processing) analysis models in Business Intelligence analysis, it is common that the users need to add new, unanticipated dimensions to the OLAP cube. In a conventional implementation, this would imply frequent re-designs of the cube's dimensions. We present an alternative method for the addition of new dimensions. Interestingly, the same design method can also be used to import EAV (Entity-Attribute-Value) tables into a cube. EAV tables have earlier been used to represent extremely sparse data in applications such as biomedical databases. Though space-efficient, EAV-representation can be awkward to query. Our EAV-to-OLAP cube methodology has an advantage of managing many-to-many relationships in a natural manner. Simple theoretical analysis shows that the methodology is efficient in space consumption. We demonstrate the efficiency of our approach in terms of the speed of OLAP cube re-processing when importing EAV-style data, comparing the performance of our cube design method with the performance of the conventional cube design. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24511-4_5 | Lecture Notes in Business Information Processing |
Keywords | DocType | Volume |
OLAP,dimensions,EAV | Conference | 90 |
ISSN | Citations | PageRank |
1865-1348 | 2 | 0.39 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Thanisch | 1 | 529 | 128.32 |
| Tapio Niemi | 2 | 163 | 18.90 |
| Marko Niinimäki | 3 | 83 | 11.43 |
| Jyrki Nummenmaa | 4 | 143 | 131.75 |