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/

数据挖掘的商业成功以及该领域吸引的巨大研究的巨大研究，这证明需要分析和理解数据远远超出了经典数据库查询。使用者通常特别感兴趣的是，对数据项的因果关系特别感兴趣，了解观察的原因和观察的原因。用于因果查询。在没有因果关系的情况下，用户必须依靠技术来挖掘数据中的统计意义模式。因果关系通常简单地从统计依赖性得出结论。这可能会得出不准确的结论。相关性不一定暗示该项目为新的数据库估算的因果数据库创造基础。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，这些因素和解释是当前数据库的范围。他们还可以利用在某些当前数据库中（例如大型工程项目的）中隐含但无法探索的causal信息。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，特别是重点Onarge Enarge Engineering数据库和科学数据库。此外，开发了用于处理因果关系和计算大量因果数据库中的说明的高效且可扩展的技术。本文既要将因果关系处理到传统数据库查询处理架构以及开发SpecialDataTaStream技术以扩展到最大数据互化的pliceplications。有关该项目的更多信息，请参见项目网页：http：//www.cs.cornell.edu/databases/causality/