Software Analysis and Evolution for Data Intensive Discovery

数据密集型发现的软件分析和演化

• 批准号: RGPIN-2019-05483
• 负责人: Schneider, Kevin
• 金额: $ 2.48万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749905
• 关键词: Software; Analysis; Evolution; Data; Intensive

Scientists are gaining access to massive and growing data sets from many different sources, including: social media, scientific instruments, experiments, computer modelling, and cyber-physical systems. Data is growing from megabytes and gigabytes to terabytes, petabytes, and beyond. Managing and analyzing such vast quantities of data poses numerous and complex challenges including collecting, storing, and accessing data; reproducing and sharing experimental approaches; scaling computational resources to ensure timely processing; identifying and managing important meta-data; and integrating a broad range of analytic and visualization techniques. An urgent and pressing concern is the ability of software systems to handle and continuously respond to the ever-increasing amount and diversity of data and take advantage of new analytic and big data technologies in an area of rapid change. The overall goal of my research is to better understand and address the challenges of software evolution so that software developers can better respond to changing and emerging user needs and more readily exploit technological advances. The proposed research will explore software designs and services that support data intensive discovery; analysis techniques that allow us to better understand large, complex software systems and how they best evolve to support data intensive discovery; and, techniques for migrating software to take advantage of big data technologies and meet data intensive discovery needs. My short-term objectives are to (1) develop software designs and services to track and manage data and processing steps in big data environments; (2) create a reference software architecture and framework to guide software development for data intensive discovery; (3) create analysis and visualization tools and techniques to better understand workflow and its use in large, complex software systems; (4) create analysis and visualization techniques and tools to understand data use and component composition in large, complex software systems; (5) produce tools and techniques for migrating software to access new big data ready software libraries and services; and (6) produce tools and techniques for modularizing software to support adaptation for data intensive discovery. The Government of Canada estimates that there are over 39,000 Information and Communications Technology (ICT) firms in Canada with over $181 billion in annual revenues. The ICT sector is expanding at a rate of nearly twice that of the total Canadian economy and the software and computer sub-sector is experiencing the fastest GDP growth (+6.4% in 2016). The results of this research program are intended to improve the competitiveness of Canadian software firms in delivering innovative, quality, and usable software that readily evolves to meet emerging information processing advances and user needs. HQP trained are in high demand, not only as software experts but also as big data experts.

科学家们正在从许多不同的来源获得大量且不断增长的数据集，包括：社交媒体、科学仪器、实验、计算机建模和网络物理系统。数据正在从MB和GB增长到TB、PB甚至更高。管理和分析如此海量的数据带来了许多复杂的挑战，包括收集、存储和访问数据；复制和共享实验方法；扩展计算资源以确保及时处理；识别和管理重要的元数据；以及集成广泛的分析和可视化技术。一个紧迫而紧迫的问题是软件系统处理和持续响应不断增长的数据量和多样性并在快速变化的领域利用新的分析和大数据技术的能力。我研究的总体目标是更好地理解和应对软件发展的挑战，以便软件开发人员能够更好地响应不断变化的和新出现的用户需求，并更容易地利用技术进步。拟议的研究将探索支持数据密集型发现的软件设计和服务；使我们能够更好地了解大型、复杂软件系统以及它们如何最好地演变以支持数据密集型发现的分析技术；以及迁移软件以利用大数据技术和满足数据密集型发现需求的技术。我的短期目标是：(1)开发软件设计和服务，以跟踪和管理大数据环境中的数据和处理步骤；(2)创建参考软件体系结构和框架，以指导数据密集型发现的软件开发；(3)创建分析和可视化工具和技术，以更好地了解工作流程及其在大型、复杂软件系统中的使用；(4)创建分析和可视化技术和工具，以了解大型、复杂软件系统中的数据使用和组件组成；(5)生产用于迁移软件的工具和技术，以访问新的大数据就绪的软件库和服务；以及(6)生产用于模块化软件以支持适应数据密集型发现的工具和技术。加拿大政府估计，加拿大有39,000多家信息和通信技术公司，年收入超过1,810亿美元。信息和通信技术部门的扩张速度几乎是加拿大经济总量的两倍，软件和计算机子部门的GDP增长最快(2016年增长6.4%)。这项研究计划的结果旨在提高加拿大软件公司在提供创新、高质量和可用的软件方面的竞争力，这些软件可以随时演变以满足不断出现的信息处理进步和用户需求。受过培训的HQP需求很大，不仅是作为软件专家，也是作为大数据专家。