An Indeterminacy Temporal Data Model based on Probability

There are many kinds of indeterminacy temporal data in temporal database. Therefore, many researchers have focused on building indeterminacy temporal data models. Unfortunately, established models can’t adequately address the challenges posed by indeterminacy temporal information, and can’t adapt to all sorts of involved applications. In this paper, we propose a temporal data model, named BPTM (Temporal model based on probability), to manage the indeterminacy temporal semantics of indeterminacy data. Firstly, we present our tuple-timestamp method to represent and store these temporal data including determinacy and indeterminacy data. Then we introduce the temporal primitives to process temporal relations needed in BPTM. A new probability method is brought forward to get potential information among these indeterminacy data. At last a query example based on CPR (Computer-based Patient Record) is given to show that our method is effective and feasible.