The goal of this project is to develop a systematic framework to enable "robust" query processing in presence of data uncertainties that arise naturally in a wide variety of application domains. Data uncertainties may take the form of missing or incomplete data, inherent noise in the data, trust or reputation scores assigned to data based on their sources of origin, or confidences in predictions made using automated modeling tools. The input uncertainties naturally lead to uncertainties in the results of any queries or analyses performed on such data. To enable robust and systematic reasoning over such uncertain query results, efficient algorithms and practical tools are developed to: (a) identify the input uncertainties to which query results are most sensitive, (b) decide how to use scarce resources like subject matter experts to resolve uncertainties in query results, and (c) incorporate user feedback to improve the robustness of the input uncertainty parameters themselves. The tools have the potential to make it easy and intuitive to process and analyze uncertain data and extract useful information from it in a wide range of real-world application domains including social media analysis, scientific and biological data management, sensor data management, web data integration, and information extraction. This project provides research opportunities for graduate and undergraduate students, and is aligned with several advanced graduate courses offered by the PIs. The prototype implementation of the framework, publications, and experimental data, will be disseminated via the project web site: www.cs.umd.edu/~amol/RPrDB.
