The relational model for database management: version 2
The relational model for database management: version 2
Object orientation: concepts, languages, databases, user interfaces
Object orientation: concepts, languages, databases, user interfaces
UML toolkit
A relational model of data for large shared data banks
Communications of the ACM
Fundamentals of Database Systems
Fundamentals of Database Systems
Database Systems: A Practical Approach to Design, Implementation, and Management
Database Systems: A Practical Approach to Design, Implementation, and Management
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
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Probabilistic Relational Data Mining (PRDM) refers to the use of Knowledge discovery in database (KDD) methods of learning probabilistic statistical models from relational data that has information about several types of objects. This is usually the case when the database has more than one table. PRDM provides techniques for discovering descriptive models, including relationships, correlations and causal dependencies, embedded in a set of objects as well as their component attributes. In essence, it is a marriage of probabilistic modeling, multi-relational database modeling, and object oriented modeling. The three modeling processes are integrated together into a data mining system to fulfill the overall modeling task, in which, intuitively speaking, database modeling plays a role of input, probabilistic modeling is like an output, and object-oriented modeling provides necessary background information.