Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An introduction to computational learning theory
An introduction to computational learning theory
Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Searching for dependencies at multiple abstraction levels
ACM Transactions on Database Systems (TODS)
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
On approximation measures for functional dependencies
Information Systems - Special issue: ADBIS 2002: Advances in databases and information systems
CORDS: automatic discovery of correlations and soft functional dependencies
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining approximate functional dependencies and concept similarities to answer imprecise queries
Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
Approximation algorithms for partial covering problems
Journal of Algorithms
ACM Transactions on Database Systems (TODS)
Efficient and effective explanation of change in hierarchical summaries
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
BHUNT: automatic discovery of Fuzzy algebraic constraints in relational data
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Extending dependencies with conditions
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Improving data quality: consistency and accuracy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Incorporating cardinality constraints and synonym rules into conditional functional dependencies
Information Processing Letters
Stream warehousing with DataDepot
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
Conditional Dependencies: A Principled Approach to Improving Data Quality
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
Analyses and Validation of Conditional Dependencies with Built-in Predicates
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
The VLDB Journal — The International Journal on Very Large Data Bases
Discovering matching dependencies
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the VLDB Endowment
Set cover algorithms for very large datasets
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Towards certain fixes with editing rules and master data
Proceedings of the VLDB Endowment
Data Auditor: exploring data quality and semantics using pattern tableaux
Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment
Differential dependencies: Reasoning and discovery
ACM Transactions on Database Systems (TODS)
Improving data quality by source analysis
Journal of Data and Information Quality (JDIQ)
X-CleLo: intelligent deterministic RFID data and event transformer
Personal and Ubiquitous Computing
Towards certain fixes with editing rules and master data
The VLDB Journal — The International Journal on Very Large Data Bases
Using functional dependencies for reducing the size of a data cube
FoIKS'12 Proceedings of the 7th international conference on Foundations of Information and Knowledge Systems
Improving the Data Quality of Drug Databases using Conditional Dependencies and Ontologies
Journal of Data and Information Quality (JDIQ)
Discovering conditional inclusion dependencies
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
Discovering conditional functional dependencies in XML data
ADC '11 Proceedings of the Twenty-Second Australasian Database Conference - Volume 115
Editorial: Efficient discovery of similarity constraints for matching dependencies
Data & Knowledge Engineering
Discovering denial constraints
Proceedings of the VLDB Endowment
Extending inclusion dependencies with conditions
Theoretical Computer Science
| Hi-index | 0.00 |
Conditional functional dependencies (CFDs) have recently been proposed as a useful integrity constraint to summarize data semantics and identify data inconsistencies. A CFD augments a functional dependency (FD) with a pattern tableau that defines the context (i.e., the subset of tuples) in which the underlying FD holds. While many aspects of CFDs have been studied, including static analysis and detecting and repairing violations, there has not been prior work on generating pattern tableaux, which is critical to realize the full potential of CFDs. This paper is the first to formally characterize a "good" pattern tableau, based on naturally desirable properties of support, confidence and parsimony. We show that the problem of generating an optimal tableau for a given FD is NP-complete but can be approximated in polynomial time via a greedy algorithm. For large data sets, we propose an "on-demand" algorithm providing the same approximation bound, that outperforms the basic greedy algorithm in running time by an order of magnitude. For ordered attributes, we propose the range tableau as a generalization of a pattern tableau, which can achieve even more parsimony. The effectiveness and efficiency of our techniques are experimentally demonstrated on real data.