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Digital sovereignty
Digital technology offers significant political, economic, and societal opportunities. At the same time, the notion of digital sovereignty has become a leitmotif in German discourse: the state’s capacity to assume its responsibilities and safeguard society’s – and individuals’ – ability to shape the digital transformation in a self-determined way. The education sector is exemplary for the challenge faced by Germany, and indeed Europe, of harnessing the benefits of digital technology while navigating concerns around sovereignty. It encompasses education as a core public good, a rapidly growing field of business, and growing pools of highly sensitive personal data. The report describes pathways to mitigating the tension between digitalization and sovereignty at three different levels – state, economy, and individual – through the lens of concrete technical projects in the education sector: the HPI Schul-Cloud (state sovereignty), the MERLOT data spaces (economic sovereignty), and the openHPI platform (individual sovereignty).
HPI Future SOC Lab – Proceedings 2020
The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2020. Selected projects have presented their results on April 21st and November 10th 2020 at the Future SOC Lab Day events.
HPI Future SOC Lab – Proceedings 2019
EN The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include...
Graph Transformation
This book constitutes the refereed proceedings of the 14th International Conference on Graph Transformation, ICGT 2021, which took place virtually during June 24-25, 2021. The 14 full papers and 2 tool papers presented in this book were carefully reviewed and selected from 26 submissions. They deal with the following topics: theoretical advances; application domains; and tool presentations.
Graph Transformation
This book constitutes the refereed proceedings of the 12th International Conference on Graph Transformation, ICGT 2019, held as part of STAF 2019, in Eindhoven, The Netherlands, in July2019. The 12 research papers and 1 tool paper presented in this book were carefully reviewed and selected from 22 submissions. The papers deal with the following topics: Theory, Analysis and Verification, Tools and Applications, and Transformation Rules Construction and Matching.
Fundamental Approaches to Software Engineering
This open access book constitutes the proceedings of the 27th International Conference on Fundamental Approaches to Software Engineering, FASE 2024, held in conjunction with ETAPS 2024 which took place in Luxembourg in April 2024. The 14 full papers included in this book were carefully reviewed and selected from 41 submission. The proceedings also include 5 short papers from the Test-Comp 2024 event that was hosted by FASE. They deal with the broad field of software engineering, focusing on requirements, design, architecture, modeling, applications of AI to software engineering and software engineering for AI-based systems, quality, model-driven engineering, processes, and software evolution.
Triple graph grammars for multi-version models
Like conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In the case of model-driven software engineering, employed versioning approaches also have to handle situations where different artifacts, that is, different models, are linked via automatic model transformations. In this report, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model, which enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis operations. Our approach is based on the well-known formalism of triple graph grammars and a previously introduced encoding of model version histories called multi-version models. In addition to showing the correctness of our approach with respect to the standard semantics of triple graph grammars, we conduct an empirical evaluation that demonstrates the potential benefit regarding execution time performance.
Modular and incremental global model management with extended generalized discrimination networks
Complex projects developed under the model-driven engineering paradigm nowadays often involve several interrelated models, which are automatically processed via a multitude of model operations. Modular and incremental construction and execution of such networks of models and model operations are required to accommodate efficient development with potentially large-scale models. The underlying problem is also called Global Model Management. In this report, we propose an approach to modular and incremental Global Model Management via an extension to the existing technique of Generalized Discrimination Networks (GDNs). In addition to further generalizing the notion of query operations employed i...
