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Data Integration for Heterogenous Datasets.

James Hendler1

  • 1The Rensselaer Institute for Data Exploration and Applications, Rensselaer Polytechnic Institute , Troy, New York.

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Summary
This summary is machine-generated.

Data analysts increasingly use external data, facing challenges with variety over volume. This "broad data" landscape requires new approaches for discovery and integration of diverse information sources.

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Area of Science:

  • Data Science
  • Information Science

Background:

  • Organizations increasingly rely on external data sources for analysis.
  • Web data availability, data linking technologies, and structured/unstructured data integration needs are driving this trend.

Purpose of the Study:

  • To provide a technical overview of the emerging "broad data" concept.
  • To explore key themes in broad data analysis, including data discovery, integration, and handling heterogeneous data.

Main Methods:

  • Technical overview and synthesis of emerging themes in broad data.
  • Exploration of data discovery, data integration, linked data, and structured/unstructured data combination.

Main Results:

  • Identifies "broad data" where data variety, not scale, limits analysis.
  • Highlights challenges and opportunities in managing and analyzing heterogeneous data.

Conclusions:

  • The "broad data" paradigm necessitates advancements in data discovery and integration techniques.
  • Effective combination of structured and unstructured data is crucial for future data analysis efforts.