The SOTA approach to engineering collective adaptive systems

Abeywickrama, Dhaminda B.; Bicocchi, Nicola; Mamei, Marco; Zambonelli, Franco

doi:10.1007/s10009-020-00554-3

Cited by 15 publications

(9 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paper in this special section "The SOTA approach to engineering collective adaptive systems" [4] by Dhaminda Abeywickrama, Nicola Bicocchi, Marco Mamei, and Franco Zambonelli presents SOTA, a goal-oriented requirements engineering and modelling method for describing the overall domain and the requirements of a collective adaptive system. SOTA focuses on modelling and analysing functional and non-functional requirements of self-adaptation, and enables the early verification of requirements, the identification of knowledge requirements for self-adaptation, and the identification of the most suitable architectural patterns for self-adaptation.…”

Section: Goal-oriented Methodsmentioning

confidence: 99%

“…The invariant refinement method IRM [19] supports the transition from early high-level requirements-specified, e.g. in SOTA and ARE, see [4,67]-to software architecture. IRM captures goals and requirements as invariants that describe desired system states over time.…”

Section: Goal-oriented Methodsmentioning

confidence: 99%

“…https://cordis.europa.eu/programme/id/FP7_ICT-2011.9.10, last accessed 2020-04-16 3. http://www.focas.eu, last accessed 2020-04-16 4. https://www.perspicuous-computing.science/, last accessed 2020-04-16 5.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Rigorous engineering of collective adaptive systems: special section

Nicola

Jähnichen

Wirsing

2020

Int J Softw Tools Technol Transfer

View full text Add to dashboard Cite

An adaptive system is able to adapt at runtime to dynamically changing environments and to new requirements. Adaptive systems can be single adaptive entities or collective ones that consist of several collaborating entities. Rigorous engineering requires appropriate methods and tools that help guaranteeing that an adaptive system lives up to its intended purpose. This paper introduces the special section on "Rigorous Engineering of Collective Adaptive Systems." It presents the seven contributions of the section and gives a short overview of the field of rigorously engineering collective adaptive systems by structuring it according to three topics: systematic development, methods and theories for modelling and analysis, and techniques for programming and operating collective adaptive systems.

show abstract

Section: Goal-oriented Methodsmentioning

confidence: 99%

Section: Goal-oriented Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Rigorous engineering of collective adaptive systems: special section

Nicola

Jähnichen

Wirsing

2020

Int J Softw Tools Technol Transfer

View full text Add to dashboard Cite

show abstract

“…In this section, we review the state of the art on CAS engineering, which aims to analyze and design the emergent behavior of large-scale situated cyber-physical systems. Example applications include crowd engineering for safe navigation and dispersal [15], smart mobility [16], situated problem solving [9], trust and reputation systems [17], robotics [18], [19], and resilient management of ICT infrastructures [20]. A recent survey of models, methods, and tools for rigorous CAS engineering can be found in [21].…”

Section: A Collective Adaptive Systems Developmentmentioning

confidence: 99%

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Casadei¹,

Fortino

Pianini³

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)-edge-fog-cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for "pulverization": applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT-edge-fog-cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings.

show abstract

“…But T5 is a costly model and can cause memory and a space issue since it is pre-trained on a large dataset. Compared to GPT and T5 models, BERT consumes less space [39,40].…”

Section: Introductionmentioning

confidence: 99%

Graph Ranked Clustering Based Biomedical Text Summarization Using Top k Similarity

Gupta¹,

Sharaff²,

Nagwani³

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort. Evaluating and selecting the most informative sentences from biomedical articles is always challenging. This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information. The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model. The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them. The proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial sentences. The quality of the framework is assessed via different parameters like information retention, coverage, readability, cohesion, and ROUGE scores in clustering and non-clustering modes. The significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory consumption. The configurable settings of combined parameters reduce execution time, enhance memory utilization, and extract relevant information outperforming other biomedical baseline models. An improvement of 17% is achieved when the proposed model is checked against similar biomedical text summarizers.

show abstract

The SOTA approach to engineering collective adaptive systems

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 15 publications

References 58 publications

Rigorous engineering of collective adaptive systems: special section

Rigorous engineering of collective adaptive systems: special section

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Graph Ranked Clustering Based Biomedical Text Summarization Using Top k Similarity

Contact Info

Product

Resources

About