III: Small: Efficient Ranking and Aggregate Query Processing for Probabilistic Data

III：小：概率数据的高效排序和聚合查询处理

• 批准号: 1212310
• 负责人: Feifei Li
• 金额: $ 18.84万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-30 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1212310&HistoricalAwards=false
• 关键词: III; Small; Efficient; Ranking; Aggregate

When dealing with massive quantities of data, ranking and aggregatequeries are powerful techniques for focusing attention on the mostimportant answers. Many applications that produce such massivequantities of data inherently introduce uncertainty in the same time,for example, probabilistic match in data integration, imprecisemeasurements from sensors, fuzzy duplicates in data cleaning,inconsistency in scientific data. Hence, the importance of thesequeries is even greater in probabilistic data, where a relation canencode exponentially many possible worlds. Uncertainty opens the gateto many possible definitions for ranking and aggregate queries. Thisproject systematically examines the underlying properties associatedwith the rich semantics of ranking and aggregate queries for largeamounts of probabilistic data. More importantly, this projectinvestigates the issue of how to design novel and scalable algorithmsfor processing these queries efficiently in various settings, such asthe offline, centralized environment, distributed systems and thestreaming model.With the emergence of probabilistic data in many important applicationdomains, the demand for understanding and processing the ranking andaggregate queries efficiently from the scientific community and beyond(e.g., government and military agencies) is expected to intensify inthe coming years. The results of this project lay down a firmfoundation for this important problem. For further information, such as publications, data sets and source code, please see the project website at http://www.cs.fsu.edu/~lifeifei/rankaggprob

在处理大量数据时，排名和聚合查询是将注意力集中在最重要答案上的强大技术。许多产生如此大量数据的应用在同一时间内在地引入了不确定性，例如，数据集成中的概率匹配、传感器的不精确测量、数据清洗中的模糊重复、科学数据中的不一致。因此，这些序列的重要性在概率数据中甚至更大，其中关系可以指数地编码许多可能的世界。不确定性为排名和聚合查询打开了许多可能的定义之门。该项目系统地研究了大量概率数据的排名和聚合查询的丰富语义相关的基本属性。更重要的是，该项目研究了如何设计新颖的和可扩展的算法，以便在各种设置中有效地处理这些查询，例如离线，集中式环境，分布式系统和流模型。随着概率数据在许多重要应用领域的出现，科学界和其他领域（例如，政府和军事机构）预计将在未来几年加剧。该项目的成果为解决这一重要问题奠定了坚实的基础。有关出版物、数据集和源代码等更多信息，请访问项目网站http://www.cs.fsu.edu/~lifeifei/rankaggprob