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Business Intent and Network Slicing Correlation Dataset from Data-Driven Perspective.

Jie Li1, Sai Zou2, Yanglong Sun3

  • 1College of Big Data and Information Engineering, Guizhou University, Gui Yang, 550000, China.

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Summary
This summary is machine-generated.

This study introduces the Business Intent and Network Slicing Correlation Dataset (BINS) to address the lack of data for intent extraction in Intent-Based Networking (IBN). BINS facilitates research in next-generation networks by correlating business intents with network slices.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Science

Background:

  • Intent-Based Networking (IBN) automates network configuration via user intents.
  • Accurate intent extraction is crucial for IBN but lacks sufficient public datasets.
  • Big data trends necessitate data-driven research for future network investigations.

Purpose of the Study:

  • To introduce the Business Intent and Network Slicing Correlation Dataset (BINS).
  • To support research in next-generation networks and Intent-Based Networking.
  • To provide annotated data correlating business intents with network slices.

Main Methods:

  • Dataset creation including business intent descriptions and annotated intent data.
  • Correlation analysis between business intents and network slices.
  • Data quality validation using Natural Language Processing (NLP) and Named Entity Recognition (NER).
  • Utilized DataProfiler for third-party data analysis and validation.

Main Results:

  • The BINS dataset was successfully created and validated for data quality and reliability.
  • The dataset contains user business intent descriptions, annotated intent data, and their correlations with network slices.
  • Confirmed the reliability of the BINS dataset through rigorous data validation processes.

Conclusions:

  • The BINS dataset is a valuable resource for advancing Intent-Based Networking research.
  • It addresses the critical need for data in network intent recognition.
  • BINS will aid researchers and practitioners in exploring application interactions and related technologies.