III: Small: BigSolver: Data-Intensive Solver Support for Big Data Exploration and Mining

III：小：BigSolver：支持大数据探索和挖掘的数据密集型求解器

• 批准号: 1526639
• 负责人: Ugur Cetintemel
• 金额: $ 50万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526639&HistoricalAwards=false
• 关键词: III; Small; BigSolver; Data; Intensive

Exploratory data analysis plays a key role in data-driven discovery in a wide range of domains including science, engineering and business. This project will enable data scientists from many domains to search and explore their large data sets far easier and faster than they do today. Rather than spending a lot time to set up exploration pipelines by combining multiple software tools, users will work with a single, general purpose and more usable system. Overall, this project will enable fundamentally richer means for data exploration and lead to significant productivity improvements; it will accelerate discovery and breakthroughs in many domains such as e-commerce, finance and science. This research will be incorporated in undergraduate and graduate coursework. The outreach activities include special research and education-focused programs that are geared towards undergraduates and high-school girls. This research proposes to design and build a new prototype database system, called BigSolver, that will uniquely integrate constraint solving and data management techniques. The result will enable rich, highly-efficient means for generic ad hoc search, exploration and mining over large multidimensional data collections. BigSolver will allow Constraint Programming (CP) machinery to run efficiently inside a DBMS without the need to extract, transform and move the data. This marriage will offer the rich expressiveness and efficiency of constraint-based search and optimization provided by modern CP solvers with the ability of Database Management Systems (DBMSs) to store and query data at scale. This work will be an early yet transformative step in enriching the functionality of database systems towards new data- and search-intensive applications. The proposed effort will develop novel approaches for synopsis-based in-memory processing, speculative solving, search query optimization, parallel processing and load balancing, as well as architectural innovations, which will collectively yield performance and usability levels that far improve those of the state of the art. For further information, see the project website at: http://database.cs.brown.edu/projects/SearchLight/

探索性数据分析在包括科学、工程和商业在内的广泛领域的数据驱动发现中起着关键作用。这个项目将使来自许多领域的数据科学家能够比现在更容易、更快地搜索和探索他们的大型数据集。用户将不再需要花费大量时间通过组合多种软件工具来建立勘探管道，而是使用一个单一的、通用的、更可用的系统。总的来说，该项目将从根本上为数据探索提供更丰富的手段，并显著提高生产力；它将加速电子商务、金融和科学等许多领域的发现和突破。本研究将纳入本科和研究生课程。外联活动包括针对本科生和高中女生的特别研究和教育重点项目。本研究建议设计并构建一个新的原型数据库系统，称为BigSolver，它将独特地集成约束求解和数据管理技术。其结果将为大型多维数据集的通用临时搜索、探索和挖掘提供丰富、高效的手段。BigSolver将允许约束编程（CP）机制在DBMS中高效运行，而不需要提取、转换和移动数据。这种结合将提供现代CP求解器所提供的基于约束的搜索和优化的丰富表现力和效率，并具有数据库管理系统（dbms）大规模存储和查询数据的能力。这项工作将是向新的数据和搜索密集型应用程序丰富数据库系统功能的早期变革步骤。提议的努力将为基于概要的内存处理、推测性解决、搜索查询优化、并行处理和负载平衡以及架构创新开发新的方法，这些方法将共同产生性能和可用性水平，大大提高当前技术水平。欲了解更多信息，请访问项目网站：http://database.cs.brown.edu/projects/SearchLight/