Reference : Modeling Smart Contracts Activities: A Tensor based Approach
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/34197
Modeling Smart Contracts Activities: A Tensor based Approach
English
Charlier, Jérémy Henri J. mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Hilger, Jean mailto [Banque et Caisse d'Epargne de l'Etat (BCEE)]
Nov-2017
Proceedings of 2017 Future Technologies Conference (FTC), 29-30 November 2017, Vancouver, Canada
Charlier, Jeremy mailto
State, Radu mailto
Hilger, Jean mailto
IEEE
49-55
Yes
9781538628232
2017 Future Technologies Conference (FTC)
from 29-11-2017 to 30-11-2017
Science and Information (SAI) Conferences
Vancouver
Canada
[en] Tensors ; CANDECOMP/PARAFAC decomposition ; stochastic processes simulation
[en] Smart contracts are autonomous software executing predefined conditions. Two of the biggest advantages of the smart contracts are secured protocols and transaction costs reduction. On the Ethereum platform, an open-source blockchainbased platform, smart contracts implement a distributed virtual machine on the distributed ledger. To avoid denial of service attacks and monetize the services, payment transactions are executed whenever code is being executed between contracts. It is thus natural to investigate if predictive analysis is capable
to forecast these interactions. We have addressed this issue and proposed an innovative application of the tensor decomposition CANDECOMP/PARAFAC to the temporal link prediction of smart contracts. We introduce a new approach leveraging stochastic processes for series predictions based on the tensor decomposition that can be used for smart contracts predictive analytics.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/34197

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