Data warehouses are nowadays widely-spread, build to provide an integrated view on data enabling enhanced analyzes. Usually, they serve as long-term memory, allowing multi-period queries. More often than not, data warehouses represent data in multidimensional data cubes, where transaction data (called cells, fact data or measures) are described in terms of master data (also called dimension members) hierarchically organized in dimensions. Data warehouses are build to deal with modifications in transaction data. They are, however, not able to deal with changes in the structure of these dimensions in a sophisticated way. In this chapter, we will discuss our approach for temporal data warehousing, called comet. This approach allows to deal with schema and instance modifications in a sophisticated way by introducing schema and instance versioning techniques. Furthermore, transformation functions are provided to map data from one version of structure into another.

Top

Chapter 4, Nova Science Publishers, ISBN 1-59033-802-2

Top