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A prediction-driven adaptation approach for self-adaptive sensor networks
TLDR
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. Expand
Enabling crowdsensing-based road condition monitoring service by intermediary
TLDR
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. Expand
Implemented Domain Model Generation
TLDR
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. Expand
Road Condition Measurement and Assessment: A Crowd Based Sensing Approach
TLDR
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. Expand
Verifying Temporal Properties of Use-Cases in Natural Language
TLDR
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. Expand
Taming the Evolution of Big Data and its Technologies in BigGIS - A Conceptual Architectural Framework for Spatio-Temporal Analytics at Scale
TLDR
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. Expand
From Textual Use-Cases to Component-Based Applications
TLDR
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. Expand
Organizational Information improves Forecast Efficiency of Correction Techniques
TLDR
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. Expand
BigGIS: a continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper)
TLDR
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. Expand
Accessing Libraries of Media Art through Metadata
TLDR
The methods presented in this paper have been implemented in the context of the project Gateway to Archives of Media Art (GAMA for short), to establish a portal for online access to some of the most important digital archives and libraries on media art in Europe. Expand
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