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Storing and sharing versions of data in multiversion data warehouse : implementation and experimental evaluation

Chmiel J., Wrembel R.

Foundations of Computing and Decision Sciences

2007

Vol. 32, No. 2

87-109

In this paper we present a technique, called Bitmap Sharing, for sharing data among multiple versions of a data warehouse (DW). To this end. vre apply bitmaps that describe DW versions a given data item is shared by. Bitmap Sharing was implemented and experimentally compared to two other advanced data sharing techniques proposed in the literature. The results demonstrate that Bitmap Sharing outperforms these techniques for: inserting data into multiple DW versions, selecting data from given DW versions, and deriving multiple DW versions with shared data.

Formal approach to modelling a multiversion data warehouse

Bębel B., Królikowski Z., Wrembel R.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2006

Vol. 54, nr 1

51-62

A data warehouse (DW) is a large centralized database that stores data integrated from multiple, usually heterogeneous external data sources (EDSs). DW content is processed by so called On-Line Analytical Processing applications, that analyze business trends, discover anomalies and hidden dependencies between data. These applications are part of decision support systems. EDSs constant1y change their content and often change their structures. These changes have to be propagated into a DW, causing its evolution. The propagation of content changes is implemented by means of materialized views. Whereas the propagation of structural changes is mainly based on temporal extensions and schema evolution, that limits the application of these techniques. our approach to handling the evolution of a DW is based on schema and data versioning. This mechanism is the core of, so called, a multiversion data warehouse. A multiversion DW is composed of the set of its versions. A single DW version is in turn composed of a schema version and the set of data described by Ibis schema version. Every DW version stores a DW stale which is valid within a certain time period. In Ibis paper we present: (1) a formal model of a multiversion data warehouse, (2) the set of operators with their formal semantics that support a DW evolution, (3) the impact analysis of the operators on DW data and user analytical queries. The presented formal model was a basis for implementing a multiversion DW prototype system.