Title :
Extraction and applications of statistical relationships in relational databases
Author_Institution :
Dept. of Comput. Sci., Southern Illinois Univ., Carbondale, IL, USA
fDate :
12/1/1996 12:00:00 AM
Abstract :
Discusses the modeling and extraction of statistical relationships among attributes. Different methods are used for the extraction of different types of relationships. A complete methodology for extraction is developed by integrating widely-accepted statistical methods. Statistical relationships manifest the embedded relationships in data, and thus lend themselves naturally to estimating unknown attribute values and detecting unlikely values. We carefully examine these applications and evaluate the usefulness of statistical relationships in these applications using a real-life database
Keywords :
data integrity; database theory; deductive databases; knowledge acquisition; relational databases; statistical databases; data mining; embedded data relationships; integrity constraints; knowledge discovery; relational databases; statistical relationships; unknown attribute values estimation; unlikely values detection; Algorithm design and analysis; Application software; Computer Society; Data mining; Machine learning; Machine learning algorithms; Relational databases; Remuneration; Statistical analysis; Statistics;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on