A Method for Implementing a Probabilistic Model as a Relational Database
This work addresses the challenge of integrating probabilistic reasoning with database systems for applications like dynamic programming and constraint propagation, but it appears incremental as it extends existing relational models.
The paper tackles the problem of implementing probabilistic inference by proposing a method that represents probability models as generalized relational databases, allowing probabilistic requests to be processed as standard relational queries, with the result being that conventional database management systems can be easily adopted for such systems.
This paper discusses a method for implementing a probabilistic inference system based on an extended relational data model. This model provides a unified approach for a variety of applications such as dynamic programming, solving sparse linear equations, and constraint propagation. In this framework, the probability model is represented as a generalized relational database. Subsequent probabilistic requests can be processed as standard relational queries. Conventional database management systems can be easily adopted for implementing such an approximate reasoning system.