III: Small: Rural: Querying Rich Uncertain Data in Real Time

三：小：农村：实时查询丰富的不确定数据

• 批准号: 1017452
• 负责人: Tingjian Ge
• 金额: --
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017452&HistoricalAwards=false
• 关键词: III; Small; Rural; Querying; Rich

Nowadays, a burgeoning amount of data is made available continuously. Example sources include various sensors, RFID, computer network traffic, phone conversations, and web searches. Much of this data is noisy, inconsistent, or even erroneous. Compared to deterministic data, uncertain data carries more information. Forcing uncertain data to be deterministic (e.g., by taking the expectations) can cause significant information loss in query results, possibly leading to wrong judgments for queries that aid decision making. Moreover, real-time decisions based on these data can have a significant impact on the quality of life, on the economy, and on our security. However, fast and real-time processing of uncertain data is a difficult problem. The broad goal of the RURAL (querying Rich Uncertain data in ReAL time) project is to provide techniques that make this query processing task feasible. To achieve this goal the project includes: (1) integrating data cleansing, distribution learning, and query processing through pipelining; (2) developing fast and online query processing algorithms on uncertain data; (3) providing a rich treatment of uncertain data distributions including compression, sharing, and gauging their reliability; (4) using predictive models to infer uncertain data distributions; and (5) answering top-k queries with consideration of typicality of results.The RURAL project extends the state of art in real-time query processing of uncertain data for decision-making. It is used by the Kentucky Transportation Center for the application of dynamic traffic routing and control, and by the Biomedical Division at the University of Kentucky for real-time monitoring applications. The research results are integrated with education through class projects in Computer Science graduate courses at the University of Kentucky and tutorials at conferences. Further information on the project can be found on the project web page: http://protocols.netlab.uky.edu/~ge/projects/rural.

如今，不断增长的数据量是可用的。示例源包括各种传感器、RFID、计算机网络流量、电话会话和web搜索。这些数据中有很多是嘈杂的、不一致的，甚至是错误的。与确定性数据相比，不确定性数据承载了更多的信息。将不确定的数据变为确定的（例如，通过取期望）可能会导致查询结果中严重的信息丢失，可能导致对有助于决策的查询的错误判断。此外，基于这些数据的实时决策可以对生活质量、经济和我们的安全产生重大影响。然而，不确定数据的快速实时处理是一个难题。RURAL（实时查询丰富的不确定数据）项目的总体目标是提供使该查询处理任务可行的技术。为了实现这一目标，该项目包括：(1)通过流水线集成数据清理、分布学习和查询处理；(2)开发不确定数据的快速在线查询处理算法；(3)提供对不确定数据分布的丰富处理，包括压缩、共享和测量其可靠性；(4)利用预测模型推断不确定数据的分布；(5)考虑结果的典型性，回答top-k查询。RURAL项目扩展了不确定数据的实时查询处理技术，用于决策。它被肯塔基运输中心用于动态交通路由和控制的应用，并被肯塔基大学生物医学部门用于实时监控应用。研究成果通过肯塔基大学计算机科学研究生课程的课堂项目和会议辅导与教育相结合。关于该项目的更多信息可以在该项目的网页上找到：http://protocols.netlab.uky.edu/~ge/projects/rural。