Abstract: Data consistency is one central issue of big data quality management research.Conditional functional depen-dencies (CFDs) are effective techniques for maintaining data consistency.In practice,different repairing sequences may affect precision and efficiency of data repairing.It is critical to select an appropriate repairing sequence.To solve the problem,based on CFDs,this paper presented a rapid decision method for repairing sequence.Firstly,a framework is designed for consistency repairing.Then,by analyzing the association between constraints,the concept of repairing sequence graph is presented to determine repairing sequence on CFDs.It contributes to avoiding some incorrect and unnecessary repairs,which can improve the accuracy of repairing.Meanwhile,repairing sequence with rules runs faster than that with real data.Furthermore,in the process of repairing sequence decision,repairing-deadlock detection is implemented to ensure the termination of repairing.Finally,compared with the existing method,this solution is more accurate and efficient evidenced by the empirical evaluation on two real-life datasets.

Key words: Data consistency,Conditional functional dependencies (CFDs),Repairing sequence