.png){kind=icon}
.png){kind=icon}
Return
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
Abstract
En 中文
Probabilistic databases address well the requirements of an increasing number of modern applications that produce large volumes of uncertain data from a variety of sources. Probabilistic keys enforce the integrity of entities in order to facilitate data processing in probabilistic database systems. For this purpose, we establish algorithms for an agile schema- and data-driven elicitation of the marginal probability by which keys should hold in a given application domain, and for reasoning about these keys. The efficiency of our elicitation framework is demonstrated theoretically and experimentally.
Keywords:
Database models
database semantics
decision problems
uncertainty
fuzzy
and probabilistic reasoning
elicitation methods
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.4
Papers:
6.7K
Citations:
3.2W