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.
Original language | English (US) |
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Pages (from-to) | 921-939 |
Number of pages | 19 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 15 |
Issue number | 4 |
DOIs | |
State | Published - Jul 2003 |
Externally published | Yes |
Funding
The authors would like to thank the reviewers for their excellent comments that greatly improved the paper. This work has been funded by ARL contract DAAL0197K0135, ARO grant DAAD190010484, DARPA/RL contract F306029910552, the ARL ADA CTA, US National Science Foundation (NSF) grant 937756, US NSF grant 01-5-24115, the Austrian Science Fund under project Z29-INF, a DFG grant, and a Marie Curie Individual Fellowship of the European Community programme “Human Potential” under contract number HPMF-CT-2001-001286 (disclaimer: The authors are solely responsible for information communicated and the European Commission is not responsible for any views or results expressed).
Keywords
- Probabilistic databases
- Temporal data
- Uncertainty management
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics