III: Large: Causal Databases

III：大型：因果数据库

• 批准号: 0911036
• 负责人: Joseph Halpern
• 金额: $ 235.31万
• 依托单位: Cornell 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0911036&HistoricalAwards=false
• 关键词: III; Large; Causal; Databases

The commercial success of data mining, and the great research interestthat this area attracts, prove that there is a need for analyzing andunderstanding data that goes well beyond classical database queries.Users are often particularly interested in understanding the causalrelationship between data items and the reasons for observations.Current database systems cannot explicitly model the causal structurewithin data (although it is often implicit in the data), and thus offerno specific support for causal queries. In the absence of informationabout causal relationships, users have to rely on techniques for miningfor statistically significant patterns in data. Causal relationshipsare often simply concluded from statistical dependencies. This can leadto inaccurate conclusions; correlation does not necessarily implycausation.This project creates the foundations for a new breed of databasescalled causal databases. Causal databases can model causal information,and allow for queries regarding causality and explanations, which arebeyond the scope of current databases. They can also take advantage ofcausal information that is implicit, but unexploited, in some currentdatabases, such as those for large engineering projects. In theproject, new database models and query languages for representing andtransforming causal information are developed, with particular focus onlarge engineering databases and scientific databases. In addition,efficient and scalable techniques for processing causality andcomputing explanations in large causal databases are developed. Thisinvolves both work on integrating causality processing into traditionaldatabase query processing architectures and the development of specialdatastream techniques for scaling up to the most data-intensiveapplications. Further information on the project can be found at the project webpage: http://www.cs.cornell.edu/databases/causality/

数据挖掘在商业上的成功以及这一领域所吸引的巨大研究兴趣证明，分析和理解数据的需求远远超出了传统的数据库查询。用户通常对理解数据项之间的因果关系和观察的原因特别感兴趣。当前的数据库系统不能显式地对数据中的因果结构建模（尽管它通常隐含在数据中），因此没有为因果查询提供特定的支持。在缺乏因果关系信息的情况下，用户不得不依赖于挖掘数据中具有统计意义的模式的技术。因果关系往往是简单地从统计依赖性。这可能导致不准确的结论;相关性并不一定意味着因果关系。这个项目为一种新的数据库奠定了基础，这种数据库被称为因果数据库。因果数据库可以对因果信息进行建模，并允许有关因果关系和解释的查询，这超出了当前数据库的范围。他们还可以利用因果信息，这是隐含的，但未开发的，在一些当前的数据库，如大型工程项目。在该项目中，开发了用于表示和转换因果信息的新数据库模型和查询语言，特别关注大型工程数据库和科学数据库。此外，有效的和可扩展的技术，处理因果关系和计算的解释，在大型因果数据库的发展。这涉及到将因果关系处理集成到传统数据库查询处理架构中的工作，以及为扩展到大多数数据密集型应用程序而开发的特殊数据流技术。有关该项目的更多信息，请访问项目网页：http://www.cs.cornell.edu/databases/causality/