This study develops the foundation for a simple, yet efficient method for uncovering functional and approximate functional dependencies in relational databases. The technique is based upon the mathematical theory of partitions defined over a relation\u27s row identifiers. Using a levelwise algorithm the minimal non-trivial functional dependencies can be found using computations conducted on integers. Therefore, the required operations on partitions are both simple and fast. Additionally, the row identifiers provide the added advantage of nominally identifying the exceptions to approximate functional dependencies, which can be used effectively in practical data mining applications
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
Abstract. This study develops the foundation for a simple, yet ecient method for uncovering function...
Abstract. This study develops the foundation for a simple, yet ecient method for uncovering function...
Data Mining (DM) represents the process of extracting interesting and previously unknown knowledge f...
International audienceIn this paper, we deal with the functional and approximate dependency inferenc...
International audienceIn this paper, we deal with the functional and approximate dependency inferenc...
Functional dependencies in relational databases are investigated. Eight binary relations, viz., (1) ...
Functional dependencies in relational databases are investigated. Eight binary relations, viz., (1) ...
Functional dependencies in relational databases are investigated. Eight binary relations, viz., (1) ...
Relational database schemas must be semantically enriched to reflect knowledge about the data, as ne...
We implemented a system to mine functional dependencies from relational databases. Our system improv...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
Abstract. This study develops the foundation for a simple, yet ecient method for uncovering function...
Abstract. This study develops the foundation for a simple, yet ecient method for uncovering function...
Data Mining (DM) represents the process of extracting interesting and previously unknown knowledge f...
International audienceIn this paper, we deal with the functional and approximate dependency inferenc...
International audienceIn this paper, we deal with the functional and approximate dependency inferenc...
Functional dependencies in relational databases are investigated. Eight binary relations, viz., (1) ...
Functional dependencies in relational databases are investigated. Eight binary relations, viz., (1) ...
Functional dependencies in relational databases are investigated. Eight binary relations, viz., (1) ...
Relational database schemas must be semantically enriched to reflect knowledge about the data, as ne...
We implemented a system to mine functional dependencies from relational databases. Our system improv...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...
International audienceThis article is an extended abstract of our work published at VLDB’2018. The f...