  Both the volume and the collection velocity of time series generated by
monitoring sensors are increasing in the Internet of Things (IoT). Data
management and analysis requires high quality and applicability of the IoT
data. However, errors are prevalent in original time series data. Inconsistency
in time series is a serious data quality problem existing widely in IoT. Such
problem could be hardly solved by existing techniques. Motivated by this, we
define an inconsistent subsequences problem in multivariate time series, and
propose an integrity data repair approach to solve inconsistent problems. Our
proposed repairing method consists of two parts: (1) we design effective
anomaly detection method to discover latent inconsistent subsequences in the
IoT time series; and (2) we develop repair algorithms to precisely locate the
start and finish time of inconsistent intervals, and provide reliable repairing
strategies. A thorough experiment on two real-life datasets verifies the
superiority of our method compared to other practical approaches. Experimental
results also show that our method captures and repairs inconsistency problems
effectively in industrial time series in complex IIoT scenarios.
