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Article

Transaction Confirmation Time Estimation in the Bitcoin Blockchain

Authors:

Chengfei LiuAuthors Info & Claims

Web Information Systems Engineering – WISE 2021: 22nd International Conference on Web Information Systems Engineering, WISE 2021, Melbourne, VIC, Australia, October 26–29, 2021, Proceedings, Part I

Pages 30 - 45

https://doi.org/10.1007/978-3-030-90888-1_3

Published: 26 October 2021 Publication History

Abstract

As Bitcoin is universally recognized as the most popular cryptocurrency, more and more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. However, transactions cannot be confirmed altogether into the next block due to the limited block capacity. One of the most demanding requirements for users to use Bitcoin is to estimate the confirmation time of a newly submitted transaction. In this paper, we propose two approaches for estimating the confirmation time for a single transaction. The first approach DcyMean makes the estimation based on the historical confirmation time of transactions included in the blockchain. The second approach CTEN is built on neural networks to estimate based on a variety of factors including the transaction itself, block states and mempool states. Finally, we conduct experiments on real Bitcoin blockchain datasets to demonstrate the effectiveness and efficiency of our proposed approaches. Each of our approaches can finish training and estimation within one block interval, demonstrating that our approaches can process real-time cases.

References

[1]

Antonopoulos, A.M.: Mastering Bitcoin: Programming the Open Blockchain. O’Reilly Media, Inc. (2017)

[2]

Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

[3]

Balsamo, S., Marin, A., Mitrani, I., Rebagliati, N.: Prediction of the consolidation delay in blockchain-based applications. In: Proceedings of the ACM/SPEC International Conference on Performance Engineering, pp. 81–92 (2021)

[4]

Buchnik Y and Friedman R FireLedger: a high throughput blockchain consensus protocol Proc. VLDB Endow. 2020 13 9 1525-1539

[5]

Bui HT, Hussain OK, Saberi M, and Hussain F Assessing the authenticity of subjective information in the blockchain: a survey and open issues World Wide Web 2021 24 2 483-509

[6]

Chaudhry M and Templeton J The queuing system M/GB/1 and its ramifications Eur. J. Oper. Res. 1981 6 57-61

[7]

Chen Z et al. SChain: a scalable consortium blockchain exploiting intra-and inter-block concurrency Proc. VLDB Endow. 2021 14 12 2799-2802

[8]

Dang, H., Dinh, T.T.A., Loghin, D., Chang, E.C., Lin, Q., Ooi, B.C.: Towards scaling blockchain systems via sharding. In: Proceedings of the 2019 International Conference on Management of Data, pp. 123–140 (2019)

[9]

El-Hindi, M., Heyden, M., Binnig, C., Ramamurthy, R., Arasu, A., Kossmann, D.: BlockchainDB-towards a shared database on blockchains. In: Proceedings of the 2019 International Conference on Management of Data, pp. 1905–1908 (2019)

[10]

Fang, M., Zhang, Z., Jin, C., Zhou, A.: High-performance smart contracts concurrent execution for permissioned blockchain using SGX. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 1907–1912. IEEE (2021)

[11]

Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., Lehmann, S.: Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and Sarcasm. arXiv preprint arXiv:1708.00524 (2017)

[12]

Fiz B, Hommes S, and State R Park JJ, Loia V, Yi G, and Sung Y Confirmation delay prediction of transactions in the bitcoin network Advances in Computer Science and Ubiquitous Computing 2018 Singapore Springer 534-539

[13]

Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324–328. IEEE (2016)

[14]

Gundlach R, Gijsbers M, Koops D, and Resing J Predicting confirmation times of bitcoin transactions ACM SIGMETRICS Perform. Eval. Rev. 2021 48 4 16-19

[15]

Gupta S, Rahnama S, Hellings J, and Sadoghi M ResilientDB: global scale resilient blockchain fabric Proc. VLDB Endow. 2020 13 6 868-883

[16]

Han, S., Xu, Z., Zeng, Y., Chen, L.: Fluid: a blockchain based framework for crowdsourcing. In: Proceedings of the 2019 International Conference on Management of Data, pp. 1921–1924 (2019)

[17]

Hao K, Xin J, Wang Z, and Wang G Outsourced data integrity verification based on blockchain in untrusted environment World Wide Web 2020 23 4 2215-2238

[18]

Hochreiter S and Schmidhuber J Long short-term memory Neural Comput. 1997 9 8 1735-1780

[19]

Kasahara S and Kawahara J Effect of bitcoin fee on transaction-confirmation process J. Ind. Manag. Optimiz. 2019 15 1 365

[20]

Kawase Y and Kasahara S Priority queueing analysis of transaction-confirmation time for bitcoin J. Ind. Manag. Optimiz. 2020 16 3 1077

[21]

Ko K, Jeong T, Maharjan S, Lee C, and Hong JW-K Zheng Z, Dai H-N, Tang M, and Chen X Prediction of bitcoin transactions included in the next block Blockchain and Trustworthy Systems 2020 Singapore Springer 591-597

[22]

Koops, D.: Predicting the confirmation time of bitcoin transactions. arXiv preprint arXiv:1809.10596 (2018)

[23]

Ma Y, Sun Y, Lei Y, Qin N, and Lu J A survey of blockchain technology on security, privacy, and trust in crowdsourcing services World Wide Web 2020 23 1 393-419

[24]

McNally, S., Roche, J., Caton, S.: Predicting the price of bitcoin using machine learning. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 339–343. IEEE (2018)

[25]

Miller DR Computation of steady-state probabilities for M/M/1 priority queues Oper. Res. 1981 29 5 945-958

[26]

Mukhopadhyay, U., Skjellum, A., Hambolu, O., Oakley, J., Yu, L., Brooks, R.: A brief survey of cryptocurrency systems. In: 2016 14th annual conference on privacy, security and trust (PST), pp. 745–752. IEEE (2016)

[27]

Nathan, S., Govindarajan, C., Saraf, A., Sethi, M., Jayachandran, P.: Blockchain meets database: design and implementation of a blockchain relational database. arXiv preprint arXiv:1903.01919 (2019)

[28]

Peng, Y., Du, M., Li, F., Cheng, R., Song, D.: FalconDB: blockchain-based collaborative database. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 637–652 (2020)

[29]

Peng, Z., Xu, C., Wang, H., Huang, J., Xu, J., Chu, X.: P2B-trace: privacy-preserving blockchain-based contact tracing to combat pandemics. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2389–2393 (2021)

[30]

Qi, X., Zhang, Z., Jin, C., Zhou, A.: BFT-store: storage partition for permissioned blockchain via erasure coding. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1926–1929. IEEE (2020)

[31]

Ruan, P., Loghin, D., Ta, Q.T., Zhang, M., Chen, G., Ooi, B.C.: A transactional perspective on execute-order-validate blockchains. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 543–557 (2020)

[32]

Sharma, A., Schuhknecht, F.M., Agrawal, D., Dittrich, J.: Blurring the lines between blockchains and database systems: the case of hyperledger fabric. In: Proceedings of the 2019 International Conference on Management of Data, pp. 105–122 (2019)

[33]

Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: International Conference on Machine Learning, pp. 843–852 (2015)

[34]

Tao, Y., Li, B., Jiang, J., Ng, H.C., Wang, C., Li, B.: On sharding open blockchains with smart contracts. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1357–1368. IEEE (2020)

[35]

Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

[36]

Wang, H., Xu, C., Zhang, C., Xu, J.: vChain: a blockchain system ensuring query integrity. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2693–2696 (2020)

[37]

Wolff RW and Yao YC Little’s law when the average waiting time is infinite Queueing Syst. 2014 76 3 267-281

[38]

Xu, C., Zhang, C., Xu, J.: vChain: enabling verifiable Boolean range queries over blockchain databases. In: Proceedings of the 2019 International Conference on Management of Data, pp. 141–158 (2019)

[39]

Xu C, Zhang C, Xu J, and Pei J SlimChain: scaling blockchain transactions through off-chain storage and parallel processing Proc. VLDB Endow. 2021 14 11 2314-2326

[40]

Xu, Z., Chen, L.: DIV: resolving the dynamic issues of zero-knowledge set membership proof in the blockchain. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2036–2048 (2021)

[41]

Yan, Y., et al.: Confidentiality support over financial grade consortium blockchain. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 2227–2240 (2020)

[42]

Yin J, Tang M, Cao J, and Wang H Apply transfer learning to cybersecurity: predicting exploitability of vulnerabilities by description Knowl.-Based Syst. 2020 210 106529

[43]

Zhang, C., Xu, C., Xu, J., Tang, Y., Choi, B.: GEM

\land

2-tree: a gas-efficient structure for authenticated range queries in blockchain. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 842–853. IEEE (2019)

[44]

Zhang Z et al. Refiner: a reliable incentive-driven federated learning system powered by blockchain Proc. VLDB Endow. 2021 14 12 2659-2662

[45]

Zhao W, Jin S, and Yue W Phung-Duc T, Kasahara S, and Wittevrongel S Analysis of the average confirmation time of transactions in a blockchain system Queueing Theory and Network Applications 2019 Cham Springer 379-388

[46]

Zhu, Y., Zhang, Z., Jin, C., Zhou, A., Qin, G., Yang, Y.: Towards rich Qery blockchain database. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM 2020, pp. 3497–3500 (2020)

[47]

Zhu, Y., Zhang, Z., Jin, C., Zhou, A., Yan, Y.: SEBDB: semantics empowered blockchain database. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1820–1831. IEEE (2019)

Cited By

Wang KCheng YTong MNiu ZPang JHan WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Exploring Unconfirmed Transactions for Effective Bitcoin Address ClusteringProceedings of the ACM Web Conference 202410.1145/3589334.3645684(1880-1891)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645684
Zhang LZhou RLiu QXu JLiu C(2022)Bitcoin Transaction Confirmation Time Prediction: A Classification ViewWeb Information Systems Engineering – WISE 202210.1007/978-3-031-20891-1_12(155-169)Online publication date: 31-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20891-1_12

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Transaction Confirmation Time Estimation in the Bitcoin Blockchain

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Web Information Systems Engineering – WISE 2021: 22nd International Conference on Web Information Systems Engineering, WISE 2021, Melbourne, VIC, Australia, October 26–29, 2021, Proceedings, Part I

Oct 2021

668 pages

ISBN:978-3-030-90887-4

DOI:10.1007/978-3-030-90888-1

Editors:
Wenjie Zhang
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
,
Lei Zou
Peking University, Beijing, China
,
Zakaria Maamar
Zayed University, Dubai, United Arab Emirates
,
Lu Chen
Swinburne University of Technology, Melbourne, VIC, Australia

© Springer Nature Switzerland AG 2021.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 26 October 2021

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Cited By

Wang KCheng YTong MNiu ZPang JHan WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Exploring Unconfirmed Transactions for Effective Bitcoin Address ClusteringProceedings of the ACM Web Conference 202410.1145/3589334.3645684(1880-1891)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645684
Zhang LZhou RLiu QXu JLiu C(2022)Bitcoin Transaction Confirmation Time Prediction: A Classification ViewWeb Information Systems Engineering – WISE 202210.1007/978-3-031-20891-1_12(155-169)Online publication date: 31-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20891-1_12

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