Collaborative Research: Planning Grant: I/UCRC for Assured and SCAlable Data Engineering (CASCADE)

合作研究：规划补助金：I/UCRC 用于有保证和可扩展的数据工程 (CASCADE)

• 批准号: 1464579
• 负责人: Kasim Candan
• 金额: $ 1.56万
• 依托单位: Arizona State 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: 2015-04-15 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464579&HistoricalAwards=false
• 关键词: Collaborative; Research; Planning; Grant; UCRC

A data and information revolution is transforming all aspects of our life, all disciplines, and all sectors of the economy. The "big data" market is predicted to reach $50 billion by 2017, with 40% and 22% market share for services and software, respectively. While products and services continue to mature, assured and scalable data services remain major challenges. This necessitates data architectures and tools that can match the scale of the data and support timely and assured decision making. The vision of the proposed NSF I/UCRC Center for Assured and SCAlable Data Engineering (CASCADE) is to enable a fundamental shift from current ad hoc approaches to the engineering of data systems, towards a principled framework for the engineering of data systems that support reliable and timely data-driven decision making. The center will support the innovation of data architectures and tools that can match the scale of the data, and that support timely and assured decision making. Methods for information integration, analytics and visualization will help non-data-experts (in governmental and commercial sectors) to make decisions and to generate value. The key audience for the proposed NSF I/UCRC Center for Assured and SCAlable Data Engineering (CASCADE) include (a) small, medium, and large companies that rely heavily on data services (especially in the finance and energy sectors), (b) small, medium, and large companies in data technologies, and (c) government agencies and regulators. CASCADE will play an important role in developing assured and scalable data technologies that in turn will enable applications and services with significant economic and environmental impact. This includes financial fraud prevention, monitoring financial supply chains and applications in the energy and sustainability sectors. The broader impacts of the proposed project will include technology and knowledge transfer to the industrial sector, graduate and undergraduate education through mentoring of PhD students, and updates to the CSE curriculum through the incorporation of research into existing undergraduate and graduate classes. CASCADE activities will train computer science students in the methodologies that support scalable and secure data engineering and will familiarize them with real world challenges in critical domains including the financial sector and clean energy. CASCADE will also contribute to diversity on the workforce through recruitment of female and minority students. If we want to fundamentally alter the way data systems are designed and significantly change current practices, we need to ensure that data analysis, data assurance, and data management technology components are developed synergistically to achieve the following targets: (1) The design and development of each component is informed by the requirements and limitations of the others; (2) Each takes full advantage of the services and capabilities provided by the others; (3) They continuously adapt as the analysis, assurance, and management contexts evolve with the needs of the deployed application systems that they all support. A key CASCADE goal is to empower domain experts and decision makers through assured and scalable data systems, and to provide reliable and timely decision making through a sense&integrate, simulate&predict, validate&interpret, and act&adapt feedback loop. This planning grant's objective is to organize a meeting with industry partners and the universities to outline a research agenda for CASCADE. The industrial/academic partnerships of CASCADE will enable new algorithms, tools, and systems that securely manage, share, access, and analyze heterogeneous sets of static or transient data to accommodate diverse security requirements, including trust, availability, confidentiality, and integrity. Through synergistic industry/academy partnerships, CASCADE will enable a strategic framework that includes multi-disciplinary teams that translate technological insights obtained from fundamental research on (a) trusted and privacy preserving data processing and analysis, (b) real-time data processing and analysis, (c) parallel and distributed data processing and analysis, and (d) high dimensional and multi-modal data processing and analysis, into new key technology elements whose different instantiations are deployed for direct impact to various critical industries including in the energy and finance sectors.

数据和信息革命正在改变我们生活的各个方面，所有学科和所有经济部门。到2017年，“大数据”市场预计将达到500亿美元，其中服务和软件的市场份额分别为40%和22%。 虽然产品和服务不断成熟，但有保证和可扩展的数据服务仍然是主要挑战。这就需要能够匹配数据规模并支持及时和有保证的决策的数据架构和工具。NSF I/UCRC Center for Assured and SCAAlable Data Engineering（CASCADE）的愿景是实现从当前的临时方法到数据系统工程的根本转变，为支持可靠和及时的数据驱动决策的数据系统工程提供原则性框架。该中心将支持数据架构和工具的创新，以匹配数据的规模，并支持及时和有保证的决策。信息整合、分析和可视化方法将帮助非数据专家（政府和商业部门）做出决策并创造价值。NSF I/UCRC Center for Assured and SCAAlable Data Engineering（CASCADE）的主要受众包括：（a）严重依赖数据服务的小型、中型和大型公司（特别是在金融和能源领域）;（B）数据技术领域的小型、中型和大型公司;以及（c）政府机构和监管机构。CASCADE将在开发有保证和可扩展的数据技术方面发挥重要作用，这些技术反过来将使应用程序和服务具有重大的经济和环境影响。这包括预防金融欺诈、监测金融供应链以及能源和可持续发展领域的应用。拟议项目的更广泛影响将包括向工业部门转让技术和知识，通过指导博士生进行研究生和本科生教育，以及通过将研究纳入现有本科生和研究生课程来更新CSE课程。CASCADE活动将培训计算机科学专业的学生学习支持可扩展和安全数据工程的方法，并使他们熟悉金融部门和清洁能源等关键领域的真实的世界挑战。CASCADE还将通过招聘女性和少数民族学生，促进劳动力的多样性。如果我们想从根本上改变数据系统的设计方式，并显著改变当前的实践，我们需要确保数据分析、数据保证和数据管理技术组件协同开发，以实现以下目标：（1）每个组件的设计和开发都受到其他组件的需求和限制的影响;（2）每个都充分利用其他应用程序提供的服务和功能;（3）随着分析、保证和管理环境随它们都支持的已部署应用程序系统的需求而发展，它们不断适应。CASCADE的一个关键目标是通过有保证和可扩展的数据系统为领域专家和决策者提供支持，并通过感知集成、模拟预测、验证解释和行动适应反馈回路提供可靠和及时的决策。该规划补助金的目标是组织一次与行业合作伙伴和大学的会议，以概述CASCADE的研究议程。CASCADE的工业/学术合作伙伴关系将使新的算法，工具和系统能够安全地管理，共享，访问和分析静态或瞬态数据的异构集，以适应不同的安全需求，包括信任，可用性，机密性和完整性。 通过协同的行业/学术合作伙伴关系，CASCADE将实现一个战略框架，其中包括多学科团队，这些团队将从基础研究中获得的技术见解转化为（a）可信和隐私保护数据处理和分析，（B）实时数据处理和分析，（c）并行和分布式数据处理和分析，以及（d）高维和多模态数据处理和分析，新的关键技术元素，其不同的实例被部署用于对包括能源和金融部门在内的各种关键行业产生直接影响。