Published: 4th December 2016|
|Preface Alexander Heußner, Aleks Kissinger and Anton Wijs|
|Type Annotation for Adaptive Systems Paolo Bottoni, Andrew Fish and Francesco Parisi Presicce||1|
|An EMOF-Compliant Abstract Syntax for Bigraphs Timo Kehrer, Christos Tsigkanos and Carlo Ghezzi||16|
|A Graph Grammar for Modelling RNA Folding Adane Letta Mamuye, Emanuela Merelli and Luca Tesei||31|
|Towards a Step Semantics for Story-Driven Modelling Géza Kulcsár and Anthony Anjorin||42|
|Incremental View Maintenance for Deductive Graph Databases Using Generalized Discrimination Networks Thomas Beyhl and Holger Giese||57|
For decades, graphs have played the role of the prominent formal model for scientists. Graph transformation systems (GTS) propose an additional framework to also formalize these graph models' dynamics in a more rigorous way. However, their cumbersome algorithmic treatment was often the reason that these modelling techniques retained their academic nature of a generic and visual front-end model for theoretical treatment, but simulations and experiments were mainly implemented in the background based on more efficient, hand tailored data structures and algorithms.
Nevertheless, the now ubiquitous multi-core computing power paired with recently published easy access cluster computing libraries (e.g., Apache Hadoop and Spark) changed the landscape of graph (transformation) based models: For example, Apache Spark includes optimized versions of massively parallel graph algorithms, making these easily accessible for even novice programmers. Thus, today, we can detect a rise of these new graph models, which, for example, are often used -- without a deeper knowledge of the algorithmic and theoretical background -- in social network analysis and aerospace engineering.
So, did we finally arrive at the golden age of directly applying graphs as models everywhere? And -- starting from our community's long running research on the theoretical underpinnings of these graph models -- how are we going to bridge our existing body of academic research on graphs as models and the technological reality?
The previous paragraph is an abridged version of one main thread of discussion that pervaded the (very interactive and discussion rich) Graphs as Models 2016 workshop.
We, as organizers, were really glad that this year's contributions covered all areas of graphs as models: From theoretical advancements in graph transformation theory, over applications of graphs and GTS in computer science, towards transdisciplinary approaches (e.g., in biology). We were also glad to win Arend Rensink for an invited talk titled Controlled Graph Rewriting -- Illustrated in GROOVE, which combined a bird's eye (meta-)view on GTS modelling with hands-on practical examples implemented with his GROOVE tool.
Retrospectively, we would subtitle this year's workshop with the big question whether graphs as models and GTS currently make their first (publicly visible, or better: next big) step towards becoming a commodity -- not only in academia, but also in industry -- and how we can actively(!) shape this ascent with our community's (academic but also applied) knowledge of the last decades.
We take this opportunity to thank the following members of the program committee for their much acclaimed work:
We thank all the authors of submitted papers for their contributions, and all authors of accepted papers for presenting their original work at the workshop. We thank the participants of the workshop for the fruitful and inspiring discussions. Finally, we thank
the editorial board of EPTCS for the given support, and the organisers of ETAPS 2016 for providing both a material and thematic roof for our workshop.