Data in the real world is often dirty. Inconsistency is an important kind of dirty data. Before repairing inconsistency, we need to detect them first. The time complexities of current inconsistency detection algorithms are super-linear to the size of data and not suitable for big data. For inconsistency detection for big data, we develop an algorithm that detects inconsistency within one-pass scan of the data according to both the functional dependency (FD) and the conditional functional dependency (CFD) in our previous work. In this paper, we propose inconsistency detection algorithms in terms of FD, CFD and Denial Constraint (DC). DCs are more expressive than FDs and CFDs. Developing the algorithm to detect the violation of DCs increases the applicability of our inconsistency detection algorithms. We compare the performance of our algorithm with the performance of implement SQL queries in MySQL and BigQuery. The experimental results indicate the high efficiency of our algorithm
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