Closures of database hypergraphs
Journal of the ACM (JACM)
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theoretical Computer Science - First International Conference on Database Theory, Rome, September 1986
Decomposing a relation into a tree of binary relations
Journal of Computer and System Sciences
Real-world applications of Bayesian networks
Communications of the ACM
An Extended Relational Data Model For Probabilistic Reasoning
Journal of Intelligent Information Systems
Automated database schema design using mined data dependencies
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Constructing the Dependency Structure of a Multiagent Probabilistic Network
IEEE Transactions on Knowledge and Data Engineering
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Fuzzy functional dependencies and Bayesian networks
Journal of Computer Science and Technology
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Theory of Relational Databases
Theory of Relational Databases
Efficient Classification across Multiple Database Relations: A CrossMine Approach
IEEE Transactions on Knowledge and Data Engineering
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Large-sample learning of bayesian networks is NP-hard
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the implication problem for probabilistic conditional independency
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Not so greedy: Randomly Selected Naive Bayes
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Relational models are the most common representation of structured data, and acyclic database theory is important in relational databases. In this paper, we propose the method for constructing the Bayesian network structure from dependencies implied in multiple relational schemas. Based on the acyclic database theory and its relationships with probabilistic networks, we are to construct the Bayesian network structure starting from implied independence information instead of mining database instances. We first give the method to find the maximum harmoniousness subset for the multi-valued dependencies on an acyclic schema, and thus the most information of conditional independencies can be retained. Further, aiming at multi-relational environments, we discuss the properties of join graphs of multiple 3NF database schemas, and thus the dependencies between separate relational schemas can be obtained. In addition, on the given cyclic join dependency, the transformation from cyclic to acyclic database schemas is proposed by virtue of finding a minimal acyclic augmentation. An applied example shows that our proposed methods are feasible.