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: 0916488
• 负责人: Feifei Li
• 金额: $ 32.88万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916488&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，排名和汇总是将注意力集中在最重要的答案上的强大技术。许多产生如此大量数据的应用程序固有地引入了不确定性，例如，数据集成中的概率匹配，传感器的不计算评估，数据清洁中的模糊重复项，科学数据的不一致。因此，这些Queries的重要性在概率数据中甚至更大，在这种关系中，关系类似于许多可能的世界。不确定性为排名和汇总查询的许多可能定义打开了。 ThisProject系统地研究了与概率数据的大量问题相关的基本属性，并具有丰富的排名和汇总查询语义。更重要的是，该项目对如何在各种环境中有效地处理这些查询的新颖和可扩展算法的问题有效地处理这些查询，例如离线，集中环境，分布式系统和theleseamsing模型，以及在许多重要的应用程序中出现概率数据的出现。预计在未来几年中将加剧。该项目的结果为这一重要问题提供了坚定的基础。有关出版物，数据集和源代码之类的更多信息，请访问项目网站http://www.cs.fsu.edu/~lifeii/rankaggpprob