@article {B{\'u}r2019,
	title = {Distributed graph queries over models@run.time for runtime monitoring of cyber-physical systems},
	journal = {International Journal on Software Tools for Technology Transfer},
	year = {2019},
	month = {09/2019},
	abstract = {<p>
	Smart cyber-physical systems (CPSs) have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous and distributed computation platform with resource-constrained devices to monitor, manage and control autonomous behavior. First, we propose a <em>distributed runtime model</em> to capture the operational state and the context information of a smart CPS using directed, typed and attributed graphs as high-level knowledge representation. The runtime model is distributed among the participating nodes, and it is consistently kept up to date in a continuously evolving environment by a time-triggered model management protocol. Our runtime models offer a (domain-specific) model query and manipulation interface over the reliable communication middleware of the Data Distribution Service (DDS) standard widely used in the CPS domain. Then, we propose to carry out distributed runtime monitoring by capturing critical properties of interest in the form of graph queries, and design a distributed graph query evaluation algorithm for evaluating such graph queries over the distributed runtime model. As the key innovation, our (1) distributed runtime model extends existing publish\&ndash;subscribe middleware (like DDS) used in real-time CPS applications by enabling the dynamic creation and deletion of graph nodes (without compile time limits). Moreover, (2) our distributed query evaluation extends existing graph query techniques by enabling query evaluation in a real-time, resource-constrained environment while still providing scalable performance. Our approach is illustrated, and an initial scalability evaluation is carried out on the MoDeS3 CPS demonstrator and the open Train Benchmark for graph queries.</p>
},
	issn = {1433-2787},
	doi = {10.1007/s10009-019-00531-5},
	url = {https://doi.org/10.1007/s10009-019-00531-5},
	author = {M{\'a}rton B{\'u}r and G{\'a}bor Szil{\'a}gyi and Andr{\'a}s V{\"o}r{\"o}s and D{\'a}niel Varr{\'o}}
}
