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Automating IoT Data Ingestion Enabling Visual Representation.

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Summary
This summary is machine-generated.

This study presents models and tools to regularize heterogeneous Internet of Things (IoT) data for visual analytics. The solution simplifies data ingestion and enables automated dashboard creation for improved data usability.

Keywords:
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Area of Science:

  • Computer Science
  • Data Science
  • Internet of Things

Background:

  • The Internet of Things (IoT) generates vast amounts of heterogeneous data from diverse devices and models.
  • Existing IoT data requires aggregation and contextualization for effective use in visual analytics, APIs, and other systems.
  • Key Performance Indicators (KPIs) also contribute to data heterogeneity, complicating analysis.

Purpose of the Study:

  • To present models and tools for simplifying and automating the ingestion and regularization of heterogeneous IoT data.
  • To address the challenge of high data heterogeneity from IoT devices and KPIs for hypercube reporting.
  • To provide users with an index of data structures and views directly usable by visual analytics tools.

Main Methods:

  • Developed a solution to analyze loaded data for extracting IoT device models, creating instances, and generating time series.
  • Ensured the proposed IoT device model is compliant with FIWARE NGSI standards.
  • Formally defined data characterization including value type, value unit, and data type.

Main Results:

  • The implemented solution enables data preparation for visual analytics and dashboarding with minimal user interaction.
  • The IoT device model and data characterization were enforced in the Snap4City dashboard wizard and tool.
  • Validated through six European pilots focused on big data for people flow and tourism monitoring, meeting HERIT-DATA project requirements.

Conclusions:

  • The developed models and tools effectively regularize heterogeneous IoT data, simplifying visual representation and dashboarding.
  • The solution, integrated into Snap4City, demonstrates GDPR compliance and multitenant architecture capabilities.
  • Further development is needed for some visual representation tools to add advanced features, but the core model meets project requirements.