Using Portfolio Theory for Automatically Processing Information about Data Quality in Data Warehouse Environments
Abstract:
Data warehouses are characterized in general by heterogeneous data sources
providing information with different levels of quality. In such environments
many data quality approaches address the importance of defining the term
“data quality” by a set of dimensions and providing according metrics.
The benefit is the additional quality information during the analytical
processing of the data.
In this paper we present a data quality model for data warehouse environments,
which is an adaptation of Markowitz’s portfolio theory. This allows the
introduction of a new kind of analytical processing using “uncertainty” about
data quality as a steering factor in the analysis. We further enhance the model
by integrating prognosis data within a conventional data warehouse to provide
risk management for new predictions.
Authors:
Robert M. Bruckner
Institute of Software Technology, Vienna University of Technology, Austria.
Josef Schiefer
Institute of Software Technology, Vienna University of Technology, Austria.
Publishing Information:
In Proceedings of the International Conference on Advances in Information Systems (ADVIS 2000), Springer Verlag, LNCS 1909, pp. 34-43, Izmir, Turkey, October 2000.