CSR---CSI. An Adaptive Programming Framework for Data and Event Driven Computation

企业社会责任---CSI。

• 批准号: 0720580
• 负责人: Beth Plale
• 金额: --
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720580&HistoricalAwards=false
• 关键词: CSR; CSI.; Adaptive; Programming; Framework

Data-driven applications in computational science react in real time to their environment in a complex detect-analyze-response cycle. These computations can often be viewed as complex data flow graphs having components that are both data- and computationally- intensive, and requiring access to live data feeds and access to large-scale computational resources. A user may cycle through multiple graphs accessing data from sensors, instruments, databases, and large collections of files in the process of discovering new knowledge. This research investigates a programming model and framework for knowledge discovery in data-driven applications. Users program the system by declarative specification of detect-analyze-response behavior. Underlying the programming model is a continuous rule-based events processor and workflow orchestration engine organized as Web services. The research formalizes an abstract model of interaction and will map the higher-level conceptualization to the events processing and workflow runtime components. It demonstrates that the model supports a unique adaptive framework where knowledge gained from the computational and data analysis can be fed back to the data event streams. The approach is validated experimentally through quantifiable metrics and by its application to two model problems: severe storm prediction where a weather forecast is triggered based on data mining results from mining radar or model data, and adaptive resource management where hardware and software resources and environment data streams are monitored for on-the-fly resource requirementsprediction.

计算科学中数据驱动的应用在复杂的检测分析响应周期中实时对其环境进行了反应。这些计算通常可以看作是具有数据和计算密集型的组件的复杂数据流图，并且需要访问实时数据供稿并访问大规模计算资源。用户可以浏览多个图形，从传感器，仪器，数据库和大量文件集合中访问数据，以发现新知识。这项研究调查了数据驱动应用程序中知识发现的编程模型和框架。用户通过声明的检测分析反应行为来对系统进行编程。编程模型的基础是一个基于规则的事件处理器和工作流编排引擎，该引擎组织为Web服务。该研究正式化了一个抽象的交互模型，并将更高级别的概念化映射到事件处理和工作流运行时组件。它表明该模型支持一个独特的自适应框架，从计算中获得的知识可以回馈到数据事件流。该方法通过可量化的指标对实验进行了验证，并将其应用于两个模型问题：根据挖掘雷达或模型数据的数据挖掘结果触发天气预报的严重暴风雨预测，以及自适应资源管理，其中硬件和软件资源和环境数据流受监控以实行资源资源需求。