Efficient accuracy evaluation for multi-modal sensed data

Data accuracy is an important aspect in sensed data quality. Thus one necessary task for data quality management is to evaluate the accuracy of sensed data. However, to our best knowledge, neither measure nor effective methods for the accuracy evaluation are proposed for multi-typed sensed data. To address the problem for accuracy evaluation, we propose a systematic method. With MSE, a parameter to measure the accuracy in statistics, we design the accuracy evaluation framework for multi-modal data. Within this framework, we classify data types into three categories and develop accuracy evaluation algorithms for each category in cases of in presence and absence of true values. Extensive experimental results show the efficiency and effectiveness of our proposed framework and algorithms.