Managing Time Consistency for Active Data Warehouse Environments
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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.
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