Comparison of Conditional Functional Dependencies using Fast CFD and CTANE Algorithms

Conditional Functional Dependencies (CFDs) are an extension of Functional Dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, we take 4 techniques for cleaning the data from sample relations. CFD Miner, is based on techniques for mining closed item sets, and is used to detect constant CFDs, namely, CFDs with constant patterns only. It provides a heuristic efficient algorithm for discovering patterns from a fixed FD. It leverages closed-item set mining to reduce search space. CTANE works well when the arity of a sample relation is small and the support threshold is high, but it scales poorly when the arity of a relation increases. Fast CFD is more efficient when the arity of a relation is large. Greedy Method formally based on the desirable properties of support and confidence. It studying the computational complexity of automatic generation of optimal tables and providing an efficient approximation algorithm. These techniques are already implemented in the previous papers. We take algorithms of these 4 techniques and find out time and space complexity of each algorithm to know which technique will be helpful in which case and display the results in the form of line and bar charts. I. Introduction Let X and Y be subsets of a relational schema R. A Functional Dependency (FD) X→Y asserts that any two tuples that agree on the values of all the attributes in X must agree on the values of all the attributes in Y. A Conditional Functional Dependency (CFD) on R is a pair (R : X→Y,Tp) where X→Y is a standard FD also referred to as the embedded FD and Tp is a pattern tableau with all attributes from X and Y i.e. Tp[A] is a constant and for all B ϵ X, Tp[B] is a constant. It is called a variable CFD if Tp[A]=-i.e. the RHS of its pattern tuple is the unnamed variable "-". However, it is necessary to have techniques in place that can automatically discover or learn CFDs from sample data, to be used as data cleaning rules for CFD based cleaning methods to be effective in practice. Prior work. The developed methods for FD discovery are CFDMiner, CTANE and FastCFD. Initially Greedy method technique also developed by using Conditional Functional Dependencies. …