Talks and Poster Presentations (with Proceedings-Entry):

K. Meixner, D. Winkler, M. Wapp, R. Rosendahl, S. Biffl:
"Investigating the Performance of selected Data Storage Concepts for AutomationML Models";
Talk: 45th Annual Conference of the IEEE Industrial Electronics Society (IECON 2019), Lisbon, Portugal, Portugal; 2019-10-14 - 2019-10-18; in: "IEEE", IEEE, (2019), ISBN: 978-1-7281-4878-6.

English abstract:
Cyber-Physical Production Systems (CPPSs) engineering relies on the effective and efficient coordination and
collaboration of participating engineers from various disciplines
implying a proficient knowledge exchange between them. AutomationML (AML) provides a standardized, XML-based data
format allowing to exchange engineering data, which receives
more and more attention as an engineering data model. When
using shared data exchange platforms, efficient data storage for
AML models is success-critical in CPPS engineering. The purpose
of this paper is to draft a novel, flexible evaluation framework
in the context of AML model storage, modification, and retrieval
and to evaluate two particular data storage paradigms, i.e., XML-
(BaseX) and graph-based (Neo4J) databases. Based on a common
best practice API, we developed a prototype solution enabling
a flexible exchange of underlying data storage paradigms for
AML models. We used an academic AML data set for the
performance evaluation of two selected database engines for
common storage tasks. First results showed that BaseX performs
better for creating, updating, and deleting operations while Neo4J
performs better for reading operations. While BaseX efficiently
supports storing and retrieving AML models, we also observed
querying limitations in the AML API. Nevertheless, in the context
of AML data storage evaluations, the selected data sets, and the
solution approach can be seen as an initial benchmark.

Created from the Publication Database of the Vienna University of Technology.