目标在线数据提取与SystemXtract

Johannes Schützel, S. Stieber, C. Haubelt, A. Uhrmacher
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引用次数: 2

摘要

复杂的系统级模拟可以产生大量的数据,其中只有部分可能是感兴趣的。当对混合原型进行实验时,由物理和模拟组件组成,数据日志会实时生成和检查。存储完整的数据日志不仅需要大量的磁盘空间,而且还需要花费大量的精力来查找特殊事件和相关的系统操作。有针对性的在线数据提取有助于即时提供感兴趣的数据。我们提出了SystemXtract,一个强大的规范语言和工具,用于在线数据提取,支持基于数据的源、值和动态阶段的约束。本文的主要贡献是展示了如何将该语言映射到基于图形的处理体系结构中,以执行指定的数据提取。实验表明,混合原型的实时执行不受工具引起的计算时间开销的影响,同时输出的数据被简化为感兴趣的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Targeted on-line data extraction with SystemXtract
Complex system-leve simulation can produce large amounts of data, of which only portions may be of interest. When experimenting with hybrid prototypes, consisting of physi-cal and simulated components, data logs are generated and inspected in real-time. Storing full data logs would not only require much disk space, it would also require much effort to find special events and related system actions afterwards. Targeted on-line data extraction helps to instantly provide the data of interest. We present SystemXtract, a power-ful specification language and tool for on-line data extrac-tion, supporting origin-, value- and dynamic phase-based constrains on the data. The main contribution of this paper is to show how the language can be mapped to a graph-based processing architecture for executing data extraction as specified. Experiments show that the tool-induced over-head in computation time is insignificant and that the real-time execution of the hybrid prototype is not compromised, while the output is reduced to the interesting data.
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