@inproceedings{8f593af97e8b4ec49a03529b4b639013,
title = "Using Reference Attribute Grammar-Controlled Rewriting for Energy Auto-Tuning",
abstract = "Cyber-physical systems react on events reported by sensors and interact with objects of the real world according to their current state and their view of the world. This view is naturally represented by a model which is continuously analysed and updated at runtime. Model analyses should be ideally concise and efficient, requiring well-founded, comprehensible implementations with efficient reasoning mechanisms. In this paper, we apply reference attribute grammar controlled rewriting to concisely implement the runtime model of an auto-tuning case study for energy optimization. Attribute functions are used to interactively perform analyses. In case of an update, our system incrementally—and, thus, efficiently—recomputes depending analyses. Since reference attribute grammar controlled rewriting builds the required dependency graphs automatically, incremental analysis comes for free.",
keywords = "attribute grammar, graph rewriting, incremental analyses, runtime model, cyber-physical system, energy auto-tuning",
author = "Christoff B{\"u}rger and Johannes Mey and Ren{\'e} Sch{\"o}ne and Sven Karol",
note = "urn:nbn:de:0074-1474-1; 10th International Workshop on
[email protected] ; Conference date: 29-09-2015",
year = "2015",
language = "English",
volume = "1474",
publisher = "CEUR-WS",
pages = "31--40",
editor = "Sebastian G{\"o}tz and Nelly Bencomo and Gordon Blair and Hui Song",
booktitle = "CEUR Workshop Proceedings (CEUR-WS.org)",
}