Sustainability focuses on the endurance of processes and products. It is perhaps most widely associated with environmental science and climate science. All engineering disciplines are involved in sustainability initiatives. For example, the engineering of smart cities must consider sustainability concerns such as mapping of natural resource availability, statistical analysis of skill bases, and transport design. Smart city engineering also must consider approaches to system-level modelling, and open data sharing across ICT platforms. Sustainability is an inherent challenge in modern systems and software engineering.
Sustainability engineering is the discipline of constructing systems that support and enable sustainability. In sustainability engineering, many different kinds of models have to be integrated. Engineering models (which are used in development) need to be combined with scientific models (which help underpin decision making).
This Dagstuhl Seminar will explore the intrinsic nature of both scientific and engineering models, the underlying differences in their respective foundations, and the challenges related to their integration, evolution and use for decision-making. The latter in particular is a key objective of the seminar, to better support both domain experts (e.g., environmental scientists) and engineers in understanding the impact of changes to both scientific and engineering models on sustainability problems. These explorations will be based on a selected real-world case study chosen by the participants in advance of the seminar.
Specific goals for the seminar include:
- Understanding the theoretical foundations of the scientific and engineering models that underpin different aspects of sustainability engineering, e.g., the foundations of climate modelling, economic models of sustainability, model-driven engineering approaches to systems.
- Understanding the different patterns of interfaces in scientific and engineering models – i.e., the different ways in which scientific and engineering models can be "hooked together"
- Understanding the different patterns of evolution in scientific and engineering models. This will also entail classification of evolutionary patterns to help better understand how these changes will propagate through the integrated models (e.g., how will a particular type of change to an ocean chemistry model impact on a coupled software model?), to inform decisions on how to manage said changes.
- Understanding how integrated models can underpin sustainability decision making, particularly to support "what-if" analysis.
Many different kinds of models, from engineering models to scientific models, have to be integrated and coordinated to support sustainability systems such as smart grid or cities, i.e., dynamically adaptable resource management systems that aim to improve the techno-economic, social, and environmental dimensions of sustainability. Scientific models help understand sustainability concerns and evaluate alternatives, while engineering models support the development of sustainability systems. As the complexity of these systems increases, many challenges are posed to the computing disciplines to make data and model-based analysis results more accessible as well as integrate scientific and engineering models while balancing trade-offs among varied stakeholders. This seminar explored the intrinsic nature of both scientific and engineering models, the underlying differences in their respective foundations, and the challenges related to their integration, evolution, analysis, and simulation including the exploration of what-if scenarios.
Sustainability systems must provide facilities for the curation and monitoring of data sets and models and enable flexible (open) data and model integration, e.g., physical laws, scientific models, regulations and preferences, possibly coming from different technological foundations, abstractions, scale, technological spaces, and world views. This also includes the continuous, automated acquisition and analysis of new data sets, as well as automated export of data sets, scenarios, and decisions. The main function is to support the generation of what-if scenarios to project the effects on the different sustainability dimensions, and support the evaluation of externalities, especially for non rapidly renewable resources. Since the predictions are necessarily probabilistic, the system must be able to assess the uncertainty inherent in all its actions and provide suitable representations of uncertainty understandable by users. In addition to generating what-if scenarios to explore alternate model instantiations, the tool should be capable of generating suggestions for how to reach user-specified goals including quantifiable impacts and driving the dynamic adaptation of sustainability systems. These powerful services must be made accessible to the population at large, regardless of their individual situation, social status, and level of education.
This seminar explored how Model-Driven Engineering (MDE) will help to develop such an approach, and in particular i) how modeling frameworks would support the integration of the various heterogeneous models, including both engineering and scientific models; ii) how domain specific languages (DSLs) would (a) support the required socio-technical coordination, i.e., engage engineers, scientists, decision makers, communities, and the general public; and (b) integrate analysis/probabilistic/user models into the control loop of smart CPS (cyber physical system). DSLs are also supposed to provide the right interface (in terms of abstractions/constructs) to be used as tools for discovering problems and evaluating ideas.
The seminar served to identify critical disciplines and stakeholders to address MDE for sustainability and the research roadmap of the MDE community with regards to the development of sustainability systems. In particular, the seminar identified and explored four key areas: 1) research challenges relevant to modeling for sustainability (M4S); 2) a multidisciplinary collection of relevant literature to provide the foundation for exploring the research challenges; 3) three case studies from different application domains that provide a vehicle for illustrating the M4S challenges and for validating relevant research techniques; and 4) the human and social aspects of M4S.
The cumulative results of the work performed at the seminar and subsequent collaborations will help to establish the required foundations for integrating engineering and scientific models, and to explore the required management facilities for evaluating what-if scenarios and driving adaptive systems. In addition, we envision to produce as an outcome of the seminar a representative case study that will be used by the community to assess and validate contributions in the field of modeling for sustainability.
- Olivier Barais (INRIA - Rennes, FR) [dblp]
- Lucy Bastin (Aston University - Birmingham, GB) [dblp]
- Christoph Becker (University of Toronto, CA) [dblp]
- Didier Beloin-Saint-Pierre (Empa-Akademie - Zürich, CH) [dblp]
- Nelly Bencomo (Aston University - Birmingham, GB) [dblp]
- Stefanie Betz (KIT - Karlsruher Institut für Technologie, DE) [dblp]
- Keith Beven (Lancaster University, GB) [dblp]
- Gordon Blair (Lancaster University, GB) [dblp]
- Gael Blondelle (Eclipse Foundation Europe GmbH - Zwingenberg, DE)
- Betty H. C. Cheng (Michigan State University - East Lansing, US) [dblp]
- Ruzanna Chitchyan (University of Bristol, GB) [dblp]
- Benoit Combemale (University of Toulouse, FR) [dblp]
- Nelly Condori-Fernandez (Free University Amsterdam, NL) [dblp]
- Letícia Duboc (Ramon Llul University, ES) [dblp]
- François Fouquet (University of Luxembourg, LU) [dblp]
- Joao Goncalves (Empa-Akademie - Zürich, CH) [dblp]
- Øystein Haugen (Ostfold University College - Halden, NO) [dblp]
- Lorenz Hilty (Universität Zürich, CH) [dblp]
- Jean-Marc Jézéquel (IRISA - Rennes, FR) [dblp]
- Eva Kern (Universität Lüneburg, DE) [dblp]
- Jörg Kienzle (McGill University - Montreal, CA) [dblp]
- Sedef Akinli Kocak (Ryerson University - Toronto, CA) [dblp]
- Peter D. Mosses (TU Delft, NL) [dblp]
- Gunter Mussbacher (McGill University - Montreal, CA) [dblp]
- Oscar M. Nierstrasz (Universität Bern, CH) [dblp]
- Richard F. Paige (University of York, GB) [dblp]
- Birgit Penzenstadler (California State University, US) [dblp]
- Bernhard Rumpe (RWTH Aachen, DE) [dblp]
- Lionel Seinturier (Lille I University, FR) [dblp]
- Noelle Selin (MIT - Cambridge, US)
- Norbert Seyff (FH Nordwestschweiz, CH) [dblp]
- Eugene Syriani (Université de Montréal - Quebec, CA) [dblp]
- Colin Venters (University of Leeds, GB) [dblp]
- Jon Whittle (Monash University - Clayton, AU) [dblp]
- Paul Young (Lancaster University, GB) [dblp]
- modelling / simulation
- society / human-computer interaction
- software engineering