Managing Time Consistency for Active Data Warehouse Environments
Abstract:
Real-world changes are generally discovered delayed by computer systems. The typical
update patterns for traditional data warehouses on an overnight or even weekly
basis enlarge this propagation delay until the information is available to
knowledge workers. Typically, traditional data warehouses focus on summarized
data (at some level) rather than detail data. For active data warehouse environments, also detailed data about individual
entities are required for checking the data conditions and triggering actions.
Hence, keeping data current and consistent in that context is not an easy task.
In this paper we present an approach for modeling conceptual time consistency
problems and introduce a data model that deals with timely delays. It supports
knowledge workers, to find out, why (or why not) an active system responded to
a certain state of the data. Therefore the model enables analytical processing
of detail data (enhanced by valid time) based on a knowledge state at a
specified instant of time. All states that were not yet knowable to the system
at that point in time are consistently ignored.
Authors:
Robert M. Bruckner
Institute of Software Technology, Vienna University of Technology, Austria.
A M. Tjoa
Institute of Software Technology, Vienna University of Technology, Austria.
Publishing Information:
In Proceedings of the Third International Conference on Data
Warehousing and Knowledge Discovery (DaWaK 2001), LNCS 2114,
Springer Verlag, pp. 254-263, Munich, Germany, September
2001.