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<p><b>Abstract</b>—A database allows its users to reduce uncertainty about the world. However, not all properties of all objects can always be stored in a database. As a result, the user may have to use probabilistic inference rules to estimate the data required for his decisions. A decision based on such estimated data may not be perfect. We call the costs associated with such suboptimal decisions the cost of incomplete information. This cost can be reduced by expanding the database to contain more information; such expansion will increase the data-related costs because of more data collection, manipulation, storage, and retrieval. A database designer must then consider the trade-off between the cost of incomplete 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 design time all the more important. In this paper, we develop probabilistic 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 explicitly includes a trade-off between the incomplete information and the data-related costs.</p>
Temporal database, data warehousing, data incompleteness, logical design, storage cost, cost of incompleteness.

D. Dey, A. N. Saharia and T. M. Barron, "A Decision Model for Choosing the Optimal Level of Storage in Temporal Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 10, no. , pp. 297-309, 1998.
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