TY - JOUR
T1 - Temporal probabilistic object bases
AU - Biazzo, Veronica
AU - Giugno, Rosalba
AU - Lukasiewicz, Thomas
AU - Subrahmanian, V. S.
N1 - Funding Information:
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).
PY - 2003/7
Y1 - 2003/7
N2 - 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.
AB - 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.
KW - Probabilistic databases
KW - Temporal data
KW - Uncertainty management
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U2 - 10.1109/TKDE.2003.1209009
DO - 10.1109/TKDE.2003.1209009
M3 - Article
AN - SCOPUS:0041347745
VL - 15
SP - 921
EP - 939
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
SN - 1041-4347
IS - 4
ER -