Abstract
As cryptocurrencies and other business blockchain applications are becoming mainstream, the amount of transactional data as well as business contracts and documents captured within various ledgers are getting bigger and bigger. Blockchains provide enterprises and consumers with greater confidence in the integrity of the captured data. This gives rise to the new level of analytics that marries the advantages of both blockchain and big data technologies to provide trusted analysis on validated and quality big data. Blockchain-based big data is a perfect source for subsequent analytics because the big data maintained on the blockchain is both secure (i.e., tamper-proof and cannot be forged) and valuable (i.e., validated and abundant). Further, data integration and advanced analysis across on-chain and off-chain data present enterprises with even more complete business insights. In this paper, we first discuss a blockchain-based business application for micro-insurance and AI marketplaces, which render blockchain-generated big data scenarios and the opportunity to develop trusted and federated AI insights across the insurers. We then also describe the design of a blockchain-powered big data analytics platform as well as our initial steps being taken along the development of this platform.
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References
Bitcoin: A Peer-to-Peer Electronic Cash System (2008). https://bitcoin.org/bitcoin.pdf
Growth Insurance Market (2016). http://www.timeslive.co.za/thetimes/article1508818.ece
Corda (2017). https://github.com/corda/corda
Direction Service (2017). https://developers.google.com/maps/documentation/directions
Ethereum (2017). https://www.ethereum.org
Hyperledger (2017). https://www.hyperledger.org
Atzori, M.: Blockchain-based architectures for the internet of things: a survey (2017). https://ssrn.com/abstract=2846810
Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J.A., Felten, E.W.: SoK: research perspectives and challenges for bitcoin and cryptocurrencies. In: Proceedings of IEEE SSP, pp. 104–121 (2015)
Dinh, T.T.A., Wang, J., Chen, G., Liu, R., Ooi, B.C., Tan, K.L.: Blockbench: a framework for analyzing private blockchains. In: Proceedings of SIGMOD, pp. 1085–1100 (2017)
Kokoris-Kogias, E., Jovanovic, P., Gasser, L., Gailly, N., Ford, B.: Omniledger: a secure, scale-out, decentralized ledger. Cryptology ePrint Archive, Report 2017/406 (2017)
Ren, Z., Cong, K., Pouwelse, J., Erkin, Z.: Implicit consensus: blockchain with unbounded throughput. CoRR abs/1705.11046 (2017)
Tschorsch, F., Scheuermann, B.: Bitcoin and beyond: a technical survey on decentralized digital currencies. IEEE Commun. Surv. Tutor. 18(3), 2084–2123 (2016)
Verma, D.C., Calo, S.B., Cirincione, G.: Distributed AI and security issues in federated environments. In: Proceedings of ICDCN Workshops, pp. 4:1–4:6 (2018)
Vo, H.T., Kundu, A., Mohania, M.: Research directions in blockchain data management and analytics. In: Proceedings of EDBT, pp. 445–448 (2018)
Vo, H.T., Mehedy, L., Mohania, M., Abebe, E.: Blockchain-based data management and analytics for micro-insurance applications. In: Proceedings of CIKM, pp. 2539–2542 (2017)
Vukolić, M.: The quest for scalable blockchain fabric: proof-of-work vs. BFT replication. In: Camenisch, J., Kesdoğan, D. (eds.) iNetSec 2015. LNCS, vol. 9591, pp. 112–125. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39028-4_9
Acknowledgement
We would like to thank Dain Liffman, Ziyuan Wang, Josh Andres, Nick Waywood, John Wagner and Ermyas Abebe for their helpful discussion about application of blockchain technology to insurance industry.
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Vo, H.T., Mohania, M., Verma, D., Mehedy, L. (2018). Blockchain-Powered Big Data Analytics Platform. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P., Somayajulu, D. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science(), vol 11297. Springer, Cham. https://doi.org/10.1007/978-3-030-04780-1_2
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DOI: https://doi.org/10.1007/978-3-030-04780-1_2
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