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ELI: an IoT-aware big data pipeline with data curation and data quality.

Francisco José de Haro-Olmo1, Alvaro Valencia-Parra2, Ángel Jesús Varela-Vaca2

  • 1Departamento de Informática, Universidad de Almería, Almería, Spain.

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|October 9, 2023
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Summary

This study introduces ELI, an IoT Big Data pipeline for data curation and quality assessment. It ensures reliable decision-making by identifying and removing low-quality IoT data in real-time and offline scenarios.

Keywords:
Big data pipelineData curationData qualityInternet of ThingsSensors

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Area of Science:

  • Data Science
  • Internet of Things (IoT)
  • Big Data Analytics

Background:

  • Analyzing IoT sensor data requires Big Data technologies, presenting challenges in data curation and quality assessment.
  • Poor data quality can lead to erroneous decision-making, increased costs, and process errors.

Purpose of the Study:

  • To present ELI, an IoT-based Big Data pipeline for data curation and usability assessment.
  • To address challenges in analyzing complex IoT sensor data for reliable decision-making.

Main Methods:

  • Developed an IoT-based Big Data pipeline integrating data transformation and integration tools.
  • Implemented a customizable Decision Model and Notation (DMN) model for data quality evaluation.
  • Evaluated the pipeline in a smart farm scenario using agricultural humidity and temperature data.

Main Results:

  • The ELI pipeline effectively performs data curation and assesses data usability in both offline and online (stream data) scenarios.
  • Consistent results were observed across offline and online data streams.
  • Performance evaluation demonstrated the pipeline's effectiveness in identifying and discarding low-quality data.

Conclusions:

  • Data curation and quality assessment are crucial for integrating IoT information and enabling meaningful insights.
  • The proposed ELI pipeline offers a usable and effective solution for managing IoT Big Data quality.
  • Customizable decision models enhance data quality measurement across multiple dimensions.