A transaction-based temporal data model that supports prediction in real-time databases

Abstract

The authors propose database support for large sets of temporal, real-time data. Prediction models are supported: transactions can write data items to the database that have been "measured in the future". Therefore, it is necessary to allow multiple instances of the same data item, with different times of measurement. This requires larger storage requirements, but can also be used for interpolation of intermediate values, and extrapolation of future values. It is recognized that temporal correctness of data is a characteristic of the usage of the data item. Real-time data requirements are specified for each individual transaction, rather than for each data item. Transactions can access multiple instances of the same data item (for extrapolation purposes), and can specify separate temporal constraints for each accessed instance. An implementation of a hard-real-time database that realizes these requirements is given, to show that the specified requirements are realistic.