RoyaltyChain: A Blockchain-Powered Big Data Framework for Accurate Music Royalties Prediction
Megh H. Shah, Parth V. Sheth, Aparna Kumari, Sudeep Tanwar, Vrajesh Chawra, Sunil Kumar · 2024
The growth of the commodification of music in the present age has made royalties allocation in an efficient, straight-forward manner to the stakeholders, in general, a complex issue. To address these challenges, this paper introduces RoyaltyChain, a new framework powered by predictive analytics and blockchain. Using factors like streaming data, social media statistics, and geographical factors, Random Forest Regression is employed to predict royalties in a big data environment, with a blockchain-backed system ensuring secure and transparent distribution. Here Ethereum blockchain-based smart contracts are designed in Remix IDE and the Interplanetary File System (IPFS) is incorporated to handle data storage cost issues in blockchain. The performance evaluation shows an increase in the overall efficiency in predicting outcomes and the high scalability of our system in comparison with the related techniques with respect to accuracy, scalability, etc. RoyaltyChain provides a scalable framework to establish efficient and fair distribution of money from the consumption of music in this increasingly complex world.