III: Large: Collaborative Research: SciDB - An Array Oriented Data Management System for Massive Scale Scientific Data

III：大型：协作研究：SciDB - 用于大规模科学数据的面向数组的数据管理系统

• 批准号: 1110370
• 负责人: Magdalena Balazinska
• 金额: $ 37.08万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1110370&HistoricalAwards=false
• 关键词: III; Large; Collaborative; Research; SciDB

This collaborative project brings together expertise of five research teams at Brown University (IIS-1111423), University of Washington (IIS-1110370), Massachusetts Institute of Technology (IIS-1111371), Portland State University (IIS-1110917) and University of Wisconsin-Madison (IIS-1111423). Scientific data management has traditionally been performed using the file system, at best using files structured according to a low-level data format. Higher-level data management infrastructure has been task-specific and not reusable in different domains, resulting in millions of dollars of duplicated implementation effort by scientists to manage their data. The goal of this project is the development of a scientific database (SciDB), a system designed and optimized for scientific applications. The aim of SciDB is to do for science what relational databases did for the business world, namely to provide a high performance, commercial-quality and scalable data management system appropriate for many science domains.In contrast to existing database systems, SciDB is based on a multidimensional array data model and includes multiple features specific to science and critical for science: provenance, uncertainty, versions, time travel, science-specific operations, and in situ data processing. No existing system offers all these features in a single, highly scalable engine. SciDB thus significantly advances the state-of-the-art in data management in addition to supporting domain scientists in data-driven knowledge discovery. The intellectual merit of SciDB is in exploring novel, high performance solutions to nested array storage, parallel array query optimization and execution, array language design, and time travel.The primary broader impact of SciDB is on the community of scientists who benefit from the tool. By keeping scientists "in the loop" in the design of the system from the outset, the project delivers software that is broadly usable to the community. The proposal also funds participation in a series of workshops that seek to engage even more of the science community. SciDB is an open-source effort, with an initial prototype (http://www.scidb.org/) already downloaded by hundreds of users. Finally, the PIs have a strong track record of delivering robust data management software that is widely used and involving students in the process, including students from under-represented groups. Further information can be found on the project web page (http://database.cs.brown.edu/projects/scidb).

该合作项目汇集了华盛顿大学布朗大学（IIS-111423）的五个研究团队（IIS-11110370），马萨诸塞州科技研究所（IIS-111371），波特兰州立大学（IIS-1111171）（IIS-11110917）和威斯康星大学 - 威斯康星大学麦迪逊大学（IIS-1111711111111111111423）。传统上，使用文件系统进行了科学数据管理，最多可以使用根据低级数据格式构造的文件。 高级数据管理基础架构是特定于任务的，并且在不同领域不可重复使用，导致科学家管理其数据的数百万美元重复的实施工作。 该项目的目的是开发科学数据库（SCIDB），该系统为科学应用设计和优化。 The aim of SciDB is to do for science what relational databases did for the business world, namely to provide a high performance, commercial-quality and scalable data management system appropriate for many science domains.In contrast to existing database systems, SciDB is based on a multidimensional array data model and includes multiple features specific to science and critical for science: provenance, uncertainty, versions, time travel, science-specific operations, and in situ data processing.现有的系统在单个高度可扩展的引擎中提供所有这些功能。因此，除了支持数据驱动的知识发现中，SCIDB除了支持领域科学家外，还可以显着提高数据管理中的最先进。 SCIDB的智力优点在于探索嵌套阵列存储的新颖，高性能解决方案，平行阵列查询优化和执行，阵列语言设计以及时间旅行。SCIDB的主要广泛影响是对受益于该工具的科学家社区的主要影响。 从一开始就将科学家“在系统的设计中”保持“循环”，该项目提供了广泛可用于社区的软件。该提案还资助了一系列试图参与更多科学界的研讨会。 SCIDB是一种开源工作，其初始原型（http://www.scidb.org/）已经由数百个用户下载。最后，PI在提供广泛使用的强大数据管理软件方面具有良好的记录，并参与了学生，包括来自代表性不足的小组的学生。可以在项目网页（http://database.cs.brown.edu/projects/scidb）上找到更多信息。