A DECISION-MODEL FOR CHOOSING THE OPTIMAL LEVEL OF STORAGE IN TEMPORAL DATABASES

A database allows its users to reduce uncertainty about the world. How ever, not all properties of all objects can always be stored in a data base. As a result, the user may have to use probabilistic inference ru les to estimate the data required for his decisions. A decision based on such estimated data may not be perfect. We call the costs associate d with such suboptimal decisions the cost of incomplete information. T his cost can be reduced by expanding the database to contain more info rmation; such expansion will increase the data-related costs because o f more data collection, manipulation, storage, and retrieval. A databa se designer must then consider the trade-off between the cost of incom plete information and the data-related costs, and choose a design that minimizes the overall cost to the organization. In temporal databases , the sheer volume of the data involved makes such a trade-off at desi gn time all the more important. In this paper, we develop probabilisti c inference rules that allow us to infer missing values in spatial, as well as temporal, dimension. We then use the framework for developing guidelines for designing and reorganizing temporal databases, which e xplicitly includes a trade-oft between the incomplete information and the data-related costs.