Metadata Management in a Multiversion Data Warehouse

Abstract

A data warehouse (DW) is supplied with data that come from external data sources (EDSs) that are production systems. EDSs, which are usually autonomous, often change not only their contents but also their structures. The evolution of external data sources has to be reflected in a DW that uses the sources. Traditional DW systems offer a limited support for handling dynamics in their structures and contents. A promising approach to this problem is based on a multiversion data warehouse (MVDW). In such a DW, every DW version includes a schema version and data consistent with its schema version. A DW version may represent a real state at certain period of time, after the evolution of EDSs or changed user requirements or the evolution of the real world. A DW version may also represent a given business scenario that is created for simulation purposes. In order to appropriately synchronize a MVDW content and structure with EDSs as well as to analyze multiversion data, a MVDW has to manage metadata. Metadata describing a MVDW are much more complex than in traditional DWs. In our approach and prototype MVDW system, a metaschema provides data structures that support: (1) monitoring EDSs with respect to content and structural changes, (2) automatic generation of processes monitoring EDSs, (3) applying the discovered EDS changes to a selected, DW version, (4) describing the structure of every DW version, (5) querying multiple DW versions of interest at the same time, (6) presenting and comparing multiversion query results.KeywordsObject Management GroupFact TableDimension InstanceLevel InstanceDimension VersionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.