III: Small: Collaborative Proposal: Towards Robust Uncertain Data Management

III：小：协作提案：迈向稳健的不确定数据管理

• 批准号: 1218367
• 负责人: Amol Deshpande
• 金额: $ 24.86万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218367&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Proposal; Towards

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: http://www.cs.umd.edu/~amol/RPrDB.

该项目的目标是开发一个系统框架，以便在各种应用程序领域中自然出现的数据不确定性中实现“健壮”的查询处理。数据不确定性的形式可能是数据缺失或不完整、数据中固有的噪声、基于数据来源分配给数据的信任或声誉分数，或者对使用自动化建模工具进行预测的信心。输入的不确定性自然会导致对此类数据执行的任何查询或分析结果的不确定性。为了能够对这些不确定的查询结果进行鲁棒性和系统性的推理，开发了高效的算法和实用的工具来：(a)识别查询结果最敏感的输入不确定性，(b)决定如何使用主题专家等稀缺资源来解决查询结果中的不确定性，以及(c)结合用户反馈来提高输入不确定性参数本身的鲁棒性。这些工具有可能使处理和分析不确定数据变得简单和直观，并从中提取有用的信息，在广泛的现实世界应用领域，包括社交媒体分析、科学和生物数据管理、传感器数据管理、网络数据集成和信息提取。该项目为研究生和本科生提供了研究机会，并与pi提供的几门高级研究生课程保持一致。框架的原型实施、出版物和实验数据将通过项目网站：http://www.cs.umd.edu/~amol/RPrDB传播。