EAGER: Scalable Big Data Analytics

EAGER：可扩展的大数据分析

• 批准号: 1343639
• 负责人: Alok Choudhary
• 金额: $ 30万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1343639&HistoricalAwards=false
• 关键词: EAGER; Scalable; Big; Data; Analytics

Big Data analytics requires bridging the gap between data-intensive computing and data-driven computing to obtain actionable insights. The former has primarily focused on optimizing data movement, reuse, organization and storage, while the latter has focused on hypothesis-driven, bottom-up data-to-discovery and the two fields have evolved somewhat independently. This exploratory project aims to investigate a holistic Ecosystem that optimizes data generation from simulations, sensors, or business processes (Transaction Step); organizes this data (possibly combining with other data) to enable reduction, pre-processing for downstream data analysis (Organization Step); performs knowledge discovery, learning and mining models from this data (Prediction Step); and leads to actions (e.g., refining models, new experiments, recommendation) (Feedback Step). Intellectual Merit: As opposed to the current practice of considering optimizations in each step in isolation, the project considers scalability and optimizations of the entire Ecosystem for big data analytics as part of the design strategy. The project aims to consider big data challenges in designing algorithms, software, analytics, and data management. This strategy contrasts with traditional approaches that first design algorithms for small data sizes and then scale them up. The project aims to treat data complexity, computational requirement, and data access patterns as a whole when designing and implementing algorithms, software and applications. Broader Impacts: The project could advance the state of the art in big data analytics across a number of key applications such as Climate Informatics and Social Media Analytics. The software resulting from the project is being made available to the broder scientific community under open source license. The project offers enhanced opportunities for education and training of graduate students and postdoctoral researchers at Northwestern University.

大数据分析需要弥合数据密集型计算和数据驱动计算之间的差距，以获得可行的见解。前者主要专注于优化数据移动，重复使用，组织和存储，而后者则集中在假设驱动的，自下而上的数据之间，并且这两个领域已经独立发展。该探索性项目旨在研究一个整体生态系统，该系统从模拟，传感器或业务流程（交易步骤）中优化数据生成；组织这些数据（可能与其他数据结合）以减少，预处理以进行下游数据分析（组织步骤）；从此数据（预测步骤）中执行知识发现，学习和采矿模型；并导致行动（例如，精炼模型，新实验，建议）（反馈步骤）。智力优点：与当前考虑在每个步骤中进行优化的实践相反，该项目考虑了整个生态系统对大数据分析的可扩展性和优化，这是设计策略的一部分。该项目旨在考虑设计算法，软件，分析和数据管理方面的大数据挑战。该策略与传统方法对形成对比，该方法首先设计了针对小数据大小的算法，然后将其扩展。该项目旨在在设计和实施算法，软件和应用程序时将数据复杂性，计算需求以及数据访问模式整体处理。更广泛的影响：该项目可以在许多关键应用程序（例如气候信息学和社交媒体分析）等许多关键应用程序中提高最新技术状态。该项目产生的软件已根据开源许可提供给Broder科学界。该项目为西北大学的研究生和博士后研究人员提供了增强的教育和培训机会。