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Evaluation of the Use of the 12 Bands vs. NDVI from Sentinel-2 Images for Crop Identification.

Sensors (Basel, Switzerland)·2023
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Related Experiment Video

Updated: Feb 2, 2026

Infant Auditory Processing and Event-related Brain Oscillations
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Model Driven Development Applied to Complex Event Processing for Near Real-Time Open Data.

Pedro J Clemente1, Adolfo Lozano-Tello2

  • 1Quercus Software Engineering Group, INTIA (Instituto de Investigación en Tecnologías Aplicadas de Extremadura), University of Extremadura, 06071 Badajoz, Spain. pjclemente@unex.es.

Sensors (Basel, Switzerland)
|November 28, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new methodology using Model-Driven Development (MDD) and Complex Event Processing (CEP) to analyze near real-time open data. The approach simplifies managing complex data streams for applications like air quality monitoring.

Keywords:
complex event processingdata analysismodel to text transformationmodel-driven developmentopen data

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Area of Science:

  • Computer Science
  • Data Science
  • Information Systems

Background:

  • Exponential growth in data generation from IoT, social networks, and communication.
  • Increasing availability of open data from public administrations for transparency and reuse.
  • Lack of suitable methodologies and tools for analyzing near real-time open data streams.

Purpose of the Study:

  • To present a methodology for analyzing near real-time open data sources.
  • To enhance data analysis abstraction levels using domain concepts.
  • To address the challenges in identifying, consuming, and analyzing dynamic open data.

Main Methods:

  • Utilizing Model-Driven Development (MDD) to manage technological complexity.
  • Employing Complex Event Processing (CEP) for analyzing data streams.
  • Introducing OpenData2CEP, a domain-specific language (DSL) with a metamodel and graphical syntax.

Main Results:

  • A methodology and DSL (OpenData2CEP) were developed to abstract and manage complex open data.
  • Model-to-text transformations enable code generation for specific platforms like CEP engines.
  • Successful application to near real-time contexts, including air quality and earthquake data analysis.

Conclusions:

  • The proposed methodology and OpenData2CEP DSL effectively raise the abstraction level for open data analysis.
  • This approach facilitates the management and analysis of heterogeneous and complex near real-time data.
  • The framework provides a scalable solution for utilizing dynamic open data in practical applications.