Small: The MCDB Database System for Managing and Modeling Uncertainty

小：用于管理和建模不确定性的 MCDB 数据库系统

• 批准号: 0915315
• 负责人: Christopher Jermaine
• 金额: $ 50万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915315&HistoricalAwards=false
• 关键词: Small; MCDB; Database; System; Managing

The MCDB Database System for Managing and Modeling UncertaintyAnalysts working with large data sets often use statistical models to``guess'' at unknown, inaccurate, or missing information associatedwith the data stored in a database. For example, an analyst for amanufacturer may wish to know, "What would my profits have been ifI'd increased my margins by 5% last year?" The answer to thisquestion depends upon the extent to which the higher prices would haveaffected each customer's demand, which is undoubtedly guessed via theapplication of some statistical model.The MCDB project is concerned with the design and implementation of aprototype database system called the "Monte Carlo Database System," or"MCDB" for short. MCDB allows an expert-level analyst or statisticianto attach arbitrary stochastic models to the database data in order to"guess" the values for unknown or inaccurate data, such as eachcustomer's unseen demand function. These stochastic models reside inthe database, and are always up-to-date in the sense that they areparameterized on the current state of the database (using eachcustomer's most recent purchases in the above example).The project attacks a number of key intellectual and scientificchallenges. Most of these are related to the fact that forperformance reasons, it is not possible to materialize one thousandstochastic instances of a one terabyte data warehouse, and query eachof them in sequence. Novel methods for avoiding such materializationsare being considered, such as skipping Monte Carlo trials that producedata which will never be used to answer a specific query. The projectalso considers statistical challenges, such as generating databaseinstances that fall far out in the tail of the answer distribution,which is necessary for specific applications such as risk assessment.Further information is available at http://mcdb.cs.rice.edu.

用于管理和建模与大型数据集一起管理和建模的MCDB数据库系统通常使用统计模型在未知，不准确或缺失的信息上与数据库中存储的数据相关联。 例如，一位制造商的分析师可能希望知道：“如果去年将我的利润增加了5％，我的利润将是多少？”对疑虑的答案取决于价格较高的程度将在多大程度上避免了每个客户的需求，这无疑是通过某种统计模型的打印来猜测的。MCDB项目与围型数据库系统的设计和实现有关，称为“ Monte Carlo Database System”或“ MCDB”。 MCDB允许专家级分析师或Statisticianto将任意随机模型附加到数据库数据上，以“猜测”未知或不准确数据的值，例如每个客户的未见需求函数。 这些随机模型位于数据库中，并且始终是最新的，因为它们在数据库的当前状态（使用上述示例中的每个Customer的最新购买）中进行了参数。 其中大多数与以下事实有关：出于表现出色的原因，不可能实现一个Terabyte数据仓库的一个千脉冲实例，并按顺序查询它们。 避免考虑此类物质化的新方法，例如跳过蒙特卡洛试验，这些试验将永远不会用于回答特定的查询。 该弹药会考虑统计挑战，例如生成在答案分布的尾部遥遥无期的数据库，这对于特定应用程序（例如风险评估）是必不可少的。Further信息可在http://mcdb.cs.rice.edu上找到。