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

计算科学中的数据驱动应用程序在一个复杂的检测-分析-响应周期中对其环境做出实时反应。这些计算通常可以被视为复杂的数据流图，其组件既是数据密集型的，也是计算密集型的，并且需要访问实时数据馈送和访问大规模计算资源。在发现新知识的过程中，用户可以在多个图表之间循环访问来自传感器、仪器、数据库和大量文件的数据。本研究探讨了数据驱动应用中知识发现的编程模型和框架。用户通过检测-分析-响应行为的声明性规范对系统进行编程。编程模型的底层是一个连续的基于规则的事件处理器和组织为Web服务的工作流编排引擎。该研究将交互的抽象模型形式化，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，在该框架中，从计算和数据分析获得的知识可以反馈给数据事件流。该方法通过可量化的度量和对两个模型问题的应用进行了实验验证：强风暴预报和自适应资源管理，其中强风暴预报是基于挖掘雷达或模型数据的数据挖掘结果来触发天气预报，而自适应资源管理是在动态资源需求预测的情况下监视硬件和软件资源和环境数据流。

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.