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A prediction-driven adaptation approach for self-adaptive sensor networks
- Ivan Dario Paez Anaya, V. Simko, Johann Bourcier, N. Plouzeau, J. Jézéquel
- Computer ScienceSEAMS
- 2 June 2014
This paper follows a proactive self-adaptation approach that aims at overcoming the limitation of reactive approaches and regulates new architecture reconfigurations and deploys them using models at runtime.
Enabling crowdsensing-based road condition monitoring service by intermediary
- Kevin Laubis, Marcel Konstantinov, V. Simko, A. Gröschel, Christof Weinhardt
- Computer ScienceElectron. Mark.
- 1 March 2019
This work develops a smart, crowd-based road condition monitoring service that establishes an intermediary between the crowd as data provider and the road authorities and road users as service customers and proves the feasibility and usability of this smart service.
Crowd sensing of road conditions and its monetary implications on vehicle navigation
- Kevin Laubis, V. Simko, Alexander Schuller
- EconomicsIntl IEEE Conferences on Ubiquitous Intelligence…
- 1 July 2016
The results show that the main factor is the amount of road segments with high roughness index, and car owners can benefit from rerouting to a smoother road profile only in regions with road roughness at least IRI ~ 4 m/km.
Implemented Domain Model Generation
This technical report describes the statistical method for deriving an initial version of the domain model directly from textual specification written in natural language using the state-of-the-art NLP tools based on Stanford CoreNLP and Apache OpenNLP.
Road Condition Measurement and Assessment: A Crowd Based Sensing Approach
A self-calibration approach that utilizes multiple statistical models trained individually for each car, which in turn get integrated into an overall view of the road segment’s IRI.
Verifying Temporal Properties of Use-Cases in Natural Language
A semi-automated method that helps iteratively write use-cases in natural language and verify consistency of behavior encoded within them and allows verifying the consistency of textual use-case specification by employing annotations in use- case steps that are transformed into temporal logic formulae and verified within a formal behavior model.
BigGIS: a continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper)
BigGIS is introduced, a predictive and prescriptive spatio-temporal analytics platform that symbiotically combines big data analytics, semantic web technologies and visual analytics methodologies to effectively model uncertainty and generate meaningful knowledge.
Taming the Evolution of Big Data and its Technologies in BigGIS - A Conceptual Architectural Framework for Spatio-Temporal Analytics at Scale
The conceptual architectural framework of BigGIS is presented, a predictive and prescriptive spatio-temporal analytics platform that integrates big data analytics, semantic web technologies and visual analytics methodologies in the authors' continuous refinement model.
From Textual Use-Cases to Component-Based Applications
This paper describes a model-driven tool allowing code of a system to be generated from use-cases in plain English, based on the model- driven development paradigm, which makes it modular and extensible, so as to allow for use- cases in multiple language styles and generation for different component frameworks.
Organizational Information improves Forecast Efficiency of Correction Techniques
The empirical results show that debiasing with forecasts correction based on organizational information can improve forecast efficiency by 56 % to a statistical approach and statistics arguing for forecast correction that rely on organizational biases instead of basic statistical approaches that harm forecast efficiency.