Simple conditions for guaranteeing higher normal forms in relational databases
ACM Transactions on Database Systems (TODS)
On the complexity of inferring functional dependencies
Discrete Applied Mathematics - Special issue on combinatorial problems in databases
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A form driven object-oriented reverse engineering methodology
Information Systems
A relational model of data for large shared data banks
Communications of the ACM
Functional and embedded dependency inference: a data mining point of view
Information Systems - Special issue on Databases: creation, management and utilization
Searching for dependencies at multiple abstraction levels
ACM Transactions on Database Systems (TODS)
Efficient Algorithms for Mining Inclusion Dependencies
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Discovery of Functional and Approximate Dependencies Using Partitions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Building Decision Trees Using Functional Dependencies
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Information-theoretic tools for mining database structure from large data sets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Learning Bayesian Networks
Enriching the ER model based on discovered association rules
Information Sciences: an International Journal
Extracting generalization hierarchies from relational databases: A reverse engineering approach
Data & Knowledge Engineering
Mining functional dependencies from data
Data Mining and Knowledge Discovery
Conditional functional dependencies for capturing data inconsistencies
ACM Transactions on Database Systems (TODS)
Discovering data quality rules
Proceedings of the VLDB Endowment
ERACER: a database approach for statistical inference and data cleaning
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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Functional dependency plays a key role in database normalization, which is a systematic process of verifying database design to ensure the nonexistence of undesirable characteristics. Bad design could incur insertion, update, and deletion anomalies that are the major cause of database inconsistency. In this paper, we propose a novel technique to discover functional dependencies from the database table. The discovered dependencies help the database designers covering up inefficiencies inherent in their design. Our discovery technique is based on the structure analysis of Bayesian network or Bayes net. Most data mining techniques applied to the problem of functional dependency discovery are rule learning and association mining. Our work is a novel attempt of applying the Bayes net to this area of application. The proposed technique can reveal functional dependencies within a reduced search space. Therefore, computational complexity is acceptable.