CIF21 DIBBs: STORM: Spatio-Temporal Online Reasoning and Management of Large Data

CIF21 DIBBs：STORM：大数据时空在线推理与管理

• 批准号: 1443046
• 负责人: Feifei Li
• 金额: $ 115.8万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-11-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1443046&HistoricalAwards=false
• 关键词: CIF21; DIBBs; STORM; Spatio; Temporal

A fundamental challenge for many research projects is the ability to handle large quantities of heterogeneous data. Data collected from different sources and time periods can be inconsistent, or stored in different formats and data management systems. Thus, a critical step in many projects is to develop a customized query and analytical engine to translate inputs. But for each new dataset, or for each new query type or analytic task for an existing dataset, a new query interface or program must be developed, requiring significant investments of time and effort. This project will develop an automatic engine for searching large, heterogeneous data collections for weather and meteorology, particularly from instruments in the western US, in a regional network called MesoWest. This project develops an automatic query and analytical engine for large, heterogeneous spatial and temporal data. This capability allows users to automatically deploy a query and analytical engine instance over their large, heterogeneous data with spatial and temporal dimensions. The system supports a simple search-box and map-like query interface that allows numerous powerful analytical queries. Techniques to make these queries robust, relevant, and highly scalable will be developed. The project also enables users to execute queries over multiple data sources simultaneously and seamlessly. The goal of the work is to dramatically simplify the management and analysis of large spatio-temporal data at different institutions, groups, and corporations.

许多研究项目面临的一个基本挑战是处理大量异构数据的能力。从不同来源和时间段收集的数据可能不一致，或者以不同的格式和数据管理系统存储。因此，许多项目中的关键步骤是开发定制的查询和分析引擎来翻译输入。 但是对于每个新数据集，或者对于现有数据集的每个新查询类型或分析任务，都必须开发新的查询接口或程序，这需要投入大量的时间和精力。 该项目将开发一个自动引擎，用于搜索大型、异构的天气和气象数据集合，特别是来自美国西部地区网络 MesoWest 的仪器。该项目为大型异构空间和时间数据开发自动查询和分析引擎。此功能允许用户在具有空间和时间维度的大型异构数据上自动部署查询和分析引擎实例。 该系统支持简单的搜索框和类似地图的查询界面，允许大量强大的分析查询。 将开发使这些查询稳健、相关且高度可扩展的技术。 该项目还使用户能够同时无缝地对多个数据源执行查询。这项工作的目标是大大简化不同机构、团体和公司对大型时空数据的管理和分析。