Incomplete information costs and database design
ACM Transactions on Database Systems (TODS)
The temporal query language TQuel
ACM Transactions on Database Systems (TODS)
Logical modeling of temporal data
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
A homogeneous relational model and query languages for temporal databases
ACM Transactions on Database Systems (TODS)
A consensus glossary of temporal database concepts
ACM SIGMOD Record
A conceptual model for the logical design of temporal databases
Decision Support Systems - Special issue on WITS '92
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
A statistical approach to incomplete information in database systems
ACM Transactions on Database Systems (TODS)
Formal semantics for time in databases
ACM Transactions on Database Systems (TODS)
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Temporal and Real-Time Databases: A Survey
IEEE Transactions on Knowledge and Data Engineering
Towards An Implementation of Database Management Systems with Temporal Support
Proceedings of the Second International Conference on Data Engineering
A complete temporal relational algebra
The VLDB Journal — The International Journal on Very Large Data Bases
Optimal Synchronization Policies for Data Warehouses
INFORMS Journal on Computing
Hi-index | 0.00 |
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.