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Department of Informatics Blockchain and Distributed Ledger Technologies

Internal transaction ETL and analysis for Polygon

Level: MA 
Responsible Person: Dr. Taehoon Kim
Keywords: Data ETL, Blockchain analytics 

This potential master student project titled "Internal Transaction ETL and Analysis for Polygon" could focus on developing an efficient Extract, Transform, Load (ETL) pipeline for internal transactions on the Polygon blockchain, followed by light-weighted analysis of the processed data. This project would involve extracting internal transaction data from Polygon nodes, transforming it into a structured format, and loading it into a suitable database or data warehouse for analysis.The ETL process would require implementing custom scripts to interact with Polygon nodes, extract raw transaction data, and process it to isolate internal transactions. These are transactions that occur within smart contract executions and are not directly visible on the blockchain, making them particularly interesting for analysis. The student would need to design a robust data model to store the extracted information, considering factors such as transaction hashes, addresses involved, value transferred, and gas used. For the analysis phase, the student could explore various aspects of internal transactions on Polygon, such as: 

  1. Frequency and volume of internal transactions over time 

  2. Identification of smart contracts generating the most internal transactions 

  3. Analysis of gas usage patterns in internal transactions 

  4. Detection of potential anomalies or suspicious activities 

The project could leverage existing blockchain ETL tools and adapt them for Polygon's specific requirements. Additionally, the student might consider implementing real-time or near-real-time data processing to provide up-to-date insights. This project would not only contribute to the growing field of blockchain data analysis but also provide valuable insights into the inner workings of the Polygon network, potentially uncovering patterns and trends that could be of interest to both academic researchers and industry practitioners. 

References: 

  1.  Polygon ETL. (n.d.). Overview. Retrieved from https://polygon-etl.readthedocs.io/en/latest/  

  2. Dysnix. (n.d.). Full-Cycle Blockchain ETL Solutions. Retrieved from https://dysnix.com/blockchain-etl  

  3.  Zhang, Y., et al. (2024). Machine Learning for Blockchain Data Analysis: Progress and Opportunities. arXiv:2404.18251v1. Retrieved from https://arxiv.org/html/2404.18251v1  

  4. Albshaier, L., Almarri, S., & Hafizur Rahman, M.M. (2024). A Review of Blockchain's Role in E-Commerce Transactions: Open Challenges, and Future Research Directions. Computers, 13(1), 27. https://doi.org/10.3390/computers13010027  

  5. Galici, R., Ordile, L., Marchesi, M., Pinna, A., & Tonelli, R. (2020). Applying the ETL Process to Blockchain Data. Prospect and Findings. Information, 11(4), 204. https://doi.org/10.3390/info11040204