III COR: Query Evaluation and View Materialization in Probabilistic Data

III COR：概率数据中的查询评估和视图具体化

• 批准号: 0713576
• 负责人: Dan Suciu
• 金额: $ 45万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0713576&HistoricalAwards=false
• 关键词: III; COR; Query; Evaluation; View

Query Evaluation and View Materialization in Probabilistic DataPI: Dan Suciu (U. Washington)Award: IIS-0713576Databases and data management tools have a deterministic semantics: a data item either is in the data set or is not. But when data comes from multiple sources,or is extracted automatically, it often contains a variety of imprecisions thatare difficult to model using the standard deterministic semantics: the same data item may have different representations in different sources and matching algorithms are imprecise; schemas differ across sources and schema matching tools are imprecise or incomplete or both; data at different sources may hold contradictory information; finally, some data, such as sensor data, is inherently probabilistic and hence imprecise. This project represents all sources of imprecision in a single uniform way, as data with a probabilisticsemantics, and extends today''s data management tools to manage efficiently data with probabilistic semantics.Re-designing databases to handle probabilistic data is a daunting task.This project studies two problems that lie at the core of probabilistic data management: the complexity of the query evaluation problem on probabilistic databases, and the view materialization problem (deciding whether a view can be materialized and whether it can be used in other query plans). The results of this research will consists of a range of fundamental techniques to be used in a general purpose probabilistic query processor.Intellectual Merits. The project makes new contributions that lie at the intersection of several disparate fields: logic, probability theory, knowledge representation, and traditional query processing and optimization. The project enhances the understanding of the query evaluation problem on probabilistic data, develops new algorithms for efficiently evaluating such queries and for materializing views, while leveraging existing database technology.Broader Impact. Searching large information spaces (the Web; large collectionsof scientific databases; Homeland Security data) is one the new and most challenging frontiers in Computer Science. The innovation that is needed to support complex searches that scale to large and heterogeneous information spaces, has to come from the data management research community. This project makes contributions to broaden our ability to search large information spaces. If successful, the project will be one of the piecesthat will help data management technology undergo a new paradigm shift, from supporting complex queries with deterministic semantics,to supporting complex explorations with probabilistic semantics.Project URL:http://www.cs.washington.edu/homes/suciu/project-probD

ProbabilityDataPI中的查询评估和视图物化：Dan Suciu（U。华盛顿）奖：IIS-0713576数据库和数据管理工具具有确定性语义：数据项要么在数据集中，要么不在数据集中。 但是当数据来自多个源或被自动提取时，它通常包含各种难以使用标准确定性语义建模的不精确性：相同的数据项在不同的源中可能具有不同的表示，并且匹配算法是不精确的;模式在不同的源中不同，并且模式匹配工具是不精确的或不完整的，或者两者兼而有之;不同源的数据可能包含矛盾的信息;最后，有些数据，如传感器数据，本身具有概率性，因此不精确。 这个项目以一种统一的方式表示所有不精确的来源，作为具有概率语义的数据，并扩展了今天的数据管理工具，以有效地管理具有概率语义的数据。重新设计数据库来处理概率数据是一项艰巨的任务。这个项目研究了概率数据管理的核心问题：概率数据库查询评估问题的复杂性，以及视图物化问题（决定视图是否可以物化以及是否可以用于其他查询计划）。这项研究的结果将包括一系列的基本技术，用于通用的概率查询处理器。该项目在几个不同领域的交叉点做出了新的贡献：逻辑，概率论，知识表示，以及传统的查询处理和优化。 该项目增强了对概率数据查询评估问题的理解，开发了新的算法，用于有效地评估此类查询和物化视图，同时利用现有的数据库技术。搜索大型信息空间（Web;大型科学数据库集合;国土安全数据）是计算机科学中最具挑战性的新领域之一。 支持扩展到大型异构信息空间的复杂搜索所需的创新必须来自数据管理研究社区。 该项目有助于扩大我们搜索大型信息空间的能力。 如果成功的话，这个项目将是帮助数据管理技术经历一个新的范式转变的一部分，从支持具有确定性语义的复杂查询，到支持具有概率语义的复杂探索。http://www.cs.washington.edu/homes/suciu/project-probD