Intent-Based Networking (IBN) is an emerging network management technology that enables automated configurations based on user intents. A critical aspect of IBN is the accurate and autonomous extraction of user intents and their translation into a language comprehensible by network management systems. However, the current scarcity of publicly available datasets for intent extraction presents significant challenges. With the rise of big data, data-driven research methods for investigating future networks have become a trend. This paper presents a Business Intent and Network Slicing Correlation Dataset (BINS) to advance research in next-generation networks. The dataset includes user business intent descriptions, annotated intent data, and correlations between business intents and network slices. We utilize natural language processing techniques based on named entity recognition and third-party data analysis tools such as DataProfiler to validate the data quality of BINS, confirming its reliability. As a cutting-edge dataset for network intent recognition, BINS will contribute to the development of IBN systems and provide valuable data resources for researchers and practitioners exploring application interactions and related technologies.
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