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Human Behavior Analysis Using Intelligent Big Data Analytics.

Muhammad Usman Tariq1, Muhammad Babar2, Marc Poulin1

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Summary
This summary is machine-generated.

This study introduces a three-layer architecture for big data analytics to process challenging social media data. It demonstrates efficient processing of Dailymotion data using Apache Spark and Hive.

Keywords:
Apache Sparkanalyticsartificial intelligencebig datahuman behavior

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Area of Science:

  • Big Data Analytics
  • Artificial Intelligence
  • Social Media Data Analysis

Background:

  • Analyzing unstructured social media data presents significant challenges due to high volume and velocity.
  • Companies like Dailymotion face difficulties in processing billions of daily user interactions.
  • Existing methods struggle with the scale and complexity of social media datasets.

Purpose of the Study:

  • To propose and demonstrate a novel three-layer architecture for efficient big data analytics.
  • To showcase the application of Apache Spark and distributed frameworks for social media data processing.
  • To validate the architecture's effectiveness using real-world data from Dailymotion.

Main Methods:

  • Developed a three-layer big data analytics architecture.
  • Utilized Apache Spark for parallel processing and distributed data handling.
  • Integrated Hive for efficient data storage and processing, accessed via Dailymotion's API.

Main Results:

  • Successfully processed large-scale social media datasets from Dailymotion.
  • Demonstrated the efficiency and logical processing capabilities of the proposed architecture.
  • Highlighted the benefits of using Apache Spark and Hive for social media data analytics.

Conclusions:

  • The proposed architecture provides an effective solution for analyzing large and complex social media datasets.
  • Apache Spark and Hive integration enables efficient and scalable big data processing.
  • This approach offers a viable mechanism for companies to derive insights from social media interactions.