Truth Discovery of Source Dependency Perception in Dynamic Scenarios

In the era of big data, obtaining large amounts of data from different sources has become increasingly easy. However, conflicts may arise among the information provided by these sources. Therefore, various truth discovery methods have been proposed to solve this problem. In practical applications, information may be generated in chronological order, such as daily or hourly updates on weather conditions in a particular location. As a result, the truth of an object and the reliability of sources may dynamically change over time. Besides, there may be dependencies among data sources and the dependencies are stable in the short term. However, existing truth discovery methods for dynamic scenarios ignore the continuity of source dependencies in the short term. To address this issue, we study the source dependency detection and the problem of data sparsity caused by removing dependent sources in dynamic scenarios, and propose an incremental model based on source dependency detection, namely SDPTD, which can dynamically update object truth values and source weights and detect source dependencies when new data arrive. Experiments on two real-world datasets and synthetic datasets demonstrate the effectiveness and efficiency of our proposed method.