1,192 research outputs found
Towards hybrid model persistence
Change-based persistence has the potential to support faster and more accurate model comparison, merging, as well as a range of analytics activities. However, reconstructing the state of a model by replaying its editing history every time the model needs to be queried or modified can get increasingly expensive as the model grows in size. In this work, we integrate change-based and state-based persistence mechanisms in a hybrid model persistence approach that delivers the best of both worlds. In this paper, we present the design of our hybrid model persistence approach and report on its impact on time and memory footprint for model loading, saving, and storage space usage
Microservices and Machine Learning Algorithms for Adaptive Green Buildings
In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings
A research roadmap towards achieving scalability in model driven engineering
International audienceAs Model-Driven Engineering (MDE) is increasingly applied to larger and more complex systems, the current generation of modelling and model management technologies are being pushed to their limits in terms of capacity and eciency. Additional research and development is imperative in order to enable MDE to remain relevant with industrial practice and to continue delivering its widely recognised productivity , quality, and maintainability benefits. Achieving scalabil-ity in modelling and MDE involves being able to construct large models and domain-specific languages in a systematic manner, enabling teams of modellers to construct and refine large models in a collaborative manner, advancing the state of the art in model querying and transformations tools so that they can cope with large models (of the scale of millions of model elements), and providing an infrastructure for ecient storage, indexing and retrieval of large models. This paper attempts to provide a research roadmap for these aspects of scalability in MDE and outline directions for work in this emerging research area
Category Theory and Model-Driven Engineering: From Formal Semantics to Design Patterns and Beyond
There is a hidden intrigue in the title. CT is one of the most abstract
mathematical disciplines, sometimes nicknamed "abstract nonsense". MDE is a
recent trend in software development, industrially supported by standards,
tools, and the status of a new "silver bullet". Surprisingly, categorical
patterns turn out to be directly applicable to mathematical modeling of
structures appearing in everyday MDE practice. Model merging, transformation,
synchronization, and other important model management scenarios can be seen as
executions of categorical specifications.
Moreover, the paper aims to elucidate a claim that relationships between CT
and MDE are more complex and richer than is normally assumed for "applied
mathematics". CT provides a toolbox of design patterns and structural
principles of real practical value for MDE. We will present examples of how an
elementary categorical arrangement of a model management scenario reveals
deficiencies in the architecture of modern tools automating the scenario.Comment: In Proceedings ACCAT 2012, arXiv:1208.430
From Sensors to Visualization Dashboards: Need for Language Composition
International audienceIn the context of the Internet of Things, the SensApp platform is designed to collect data from sensors and support the building of associated monitoring dashboards. Bridging the gap between sensors and visualization involves up to eleven kind of models, from state machine modeling the behavior of a sensor to task diagrams modeling the actions of the end-user. This paper describes this case study, emphasizing the need for domain specific modeling language composition mechanisms to support the activity of modeling modern software-intensive systems
SOTIF-Compliant Scenario Generation Using Semi-Concrete Scenarios and Parameter Sampling
The SOTIF standard (ISO 21448) requires scenario-based testing to verify and
validate Advanced Driver Assistance Systems and Automated Driving Systems but
does not suggest any practical way to do so effectively and efficiently.
Existing scenario generation approaches either focus on exploring or exploiting
the scenario space. This generally leads to test suites that cover many known
cases but potentially miss edge cases or focused test suites that are effective
but also contain less diverse scenarios. To generate SOTIF-compliant test
suites that achieve higher coverage and find more faults, this paper proposes
semi-concrete scenarios and combines them with parameter sampling to adequately
balance scenario space exploration and exploitation. Semi-concrete scenarios
enable combinatorial scenario generation techniques that systematically explore
the scenario space, while parameter sampling allows for the exploitation of
continuous parameters. Our experimental results show that the proposed concept
can generate more effective test suites than state-of-the-art coverage-based
sampling. Moreover, our results show that including a feedback mechanism to
drive parameter sampling further increases test suites' effectiveness.Comment: accepted at IEEE ITSC 202
Towards Transparent Combination of Model Management Execution Strategies for Low-Code Development Platforms
International audienceLow-code development platforms are taking an important place in the model-driven engineering ecosystem, raising new challenges, among which transparent efficiency or scalability. Indeed, the increasing size of models leads to difficulties in interacting with them efficiently. To tackle this scalability issue, some tools are built upon specific computational strategies exploiting reactivity, or parallelism. However, their performances may vary depending on the specific nature of their usage. Choosing the most suitable computational strategy for a given usage is a difficult task which should be automated. Besides, the most efficient solutions may be obtained by the use of several strategies at the same time. is paper motivates the need for a transparent multi-strategy execution mode for model-management operations. We present an overview of the different computational strategies used in the model-driven engineering ecosystem, and use a running example to introduce the benefits of mixing strategies for performing a single computation. is example helps us present our design ideas for a multi-strategy model-management system. e code-related and DevOps challenges that emerged from this analysis are also presented
What we know and what we do not know about DMN
The recent Decision Model and Notation (DMN) establishes business decisions as first-class citizens of executable business processes. This research note has two objectives: first, to describe DMN's technical and theoretical foundations; second, to identify research directions for investigating DMN's potential benefits on a technological, individual and organizational level. To this end, we integrate perspectives from management science, cognitive theory and information systems research
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