Open AccessA Big Data Solution to Detect Conditional Functional Dependency Violations
Author(s) -
G. Somasekhar,
K. Karthikeyan
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.10.26641
Subject(s) - big data , scalability , computer science , dependency (uml) , functional dependency , dependency theory (database theory) , state (computer science) , data mining , theoretical computer science , artificial intelligence , algorithm , database , relational database
The violation detection of conditional functional dependencies in distributed environment has been a research problem giving inspiration to many researchers recently. A very few solutions were given in the recent past to handle conditional functional dependencies. Unfortunately, these are inappropriate in real time big data applications. This article mainly focuses on the big data solution to such type of problems. The proposed IMRCFDHBD algorithm reduces elapsed time and provides scalability with minimum data shipment. The result proves that the algorithm outperforms the state-of-the-art techniques in the big data scenarios.