# Temporal Probabilistic Object Bases

*Veronica Biazzo‚ Rosalba Giugno‚ Thomas Lukasiewicz and V. S. Subrahmanian*

### Abstract

There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In *explicit* TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In *implicit* TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances.