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DISCRN: A Distributed Storytelling Framework for Intelligence Analysis.

Manu Shukla1, Raimundo Dos Santos2, Feng Chen3

  • 11 Virginia Tech, Falls Church, Virginia.

Big Data
|September 22, 2017
PubMed
Summary
This summary is machine-generated.

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This study introduces DISCRN, a distributed framework for spatiotemporal storytelling that enhances entity relationship analysis. DISCRN efficiently processes massive datasets, enabling deeper insights into complex storylines from diverse data sources.

Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Storytelling, including spatiotemporal variants, relies on entity relationships but faces computational bottlenecks with large datasets.
  • Sequential processing of spatiotemporal computations becomes intractable due to the scale of entities and complexity.

Purpose of the Study:

  • To present DISCRN (distributed spatiotemporal ConceptSearch-based storytelling), a novel distributed framework for efficient spatiotemporal storytelling.
  • To address the scalability and performance issues in analyzing complex storylines from massive datasets.

Main Methods:

  • Developed a distributed framework (DISCRN) utilizing a novel ConceptSearch algorithm for entity extraction and relationship linking.
  • Employed a key-value pair paradigm and parallelization techniques for distributed spatiotemporal storytelling.
Keywords:
big datadistributed graph searchdistributed learningstorytelling

Related Experiment Videos

  • Integrated microblog data (e.g., Twitter) and event data (e.g., GDELT) for analysis.

    • Main Results:

    • DISCRN demonstrates efficient distributed spatiotemporal storytelling by processing massive datasets.
    • Novel parallelization techniques significantly accelerate the generation and filtering of storylines.
    • The framework enables deeper and broader analysis of storylines derived from real-world data.

    Conclusions:

    • DISCRN offers an efficient and scalable solution for spatiotemporal storytelling.
    • The framework's parallelization techniques are effective for handling large-scale entity relationship analysis.
    • DISCRN facilitates advanced insights from microblog and event data.