Algorithms for inferring functional dependencies from relations
Data & Knowledge Engineering
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Efficient Discovery of Functional Dependencies and Armstrong Relations
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
The Average Length of Keys and Functional Dependencies in (Random) Databases
ICDT '95 Proceedings of the 5th International Conference on Database Theory
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
Database dependency discovery: a machine learning approach
AI Communications
Efficient Algorithms for Mining Inclusion Dependencies
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
An Axiomatic Approach to Defining Approximation Measures for Functional Dependencies
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Improving Query Evaluation with Approximate Functional Dependency Based Decompositions
BNCOD 19 Proceedings of the 19th British National Conference on Databases: Advances in Databases
Differential dependencies: Reasoning and discovery
ACM Transactions on Database Systems (TODS)
Interactively eliciting database constraints and dependencies
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Functional dependency discovery via Bayes net analysis
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Towards a parallel approach for incremental mining of functional dependencies on multi-core systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
A parallel algorithm for computing borders
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the 21st ACM international conference on Information and knowledge management
Comparable dependencies over heterogeneous data
The VLDB Journal — The International Journal on Very Large Data Bases
Editorial: Efficient discovery of similarity constraints for matching dependencies
Data & Knowledge Engineering
Discovering denial constraints
Proceedings of the VLDB Endowment
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The problem of discovering functional dependencies (FDs) from an existing relation instance has received considerable attention in the database research community. To date, even the most efficient solutions have exponential complexity in the number of attributes of the instance. We develop an algorithm, FastFDs, for solving this problem based on a depth-first, heuristic-driven (DFHD) search for finding minimal covers of hypergraphs. The technique of reducing the FD discovery problem to the problem of finding minimal covers of hypergraphs was applied previously by Lopes et al. in the algorithm Dep-Miner. Dep-Miner employs a levelwise search for minimal covers, whereas FastFDs uses DFHD search. We report several tests on distinct benchmark relation instances involving Dep-Miner, FastFDs, and TANE. Our experimental results indicate that DFHD search is more efficient than Dep-Miner's levelwise search or TANE's partitioning approach for many of these benchmark instances.