A Sampling-Based Approach to Information Recovery

There has been a recent resurgence of interest in research on noisy and incomplete data. Many applications require information to be recovered from such data. For example, in sensor data processing, errors may occur in collection and transmission; in privacy-preserving data publishing, only summarized or perturbed data is available for analysis. Ideally, an approach for information recovery should have the following features. First, it should be able to incorporate prior knowledge about the data, even if such knowledge is in the form of complex distributions and constraints for which no close-form solutions exist. Second, it should be able to capture complex correlations and quantify the degree of uncertainty in the recovered data, and further support queries over such data. The database community has developed a number of approaches for information recovery, but none is general enough to offer all above features. To overcome the limitations, we take a significantly more general appro...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Junyi Xie, Jun Yang 0001, Yuguo Chen, Haixun Wang, Philip S. Yu
• Keywords: Database, ICDE 2008, Incomplete Data, Perturbed Data, Sensor Data Processing