[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.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Disciplines :
Computer science
Author, co-author :
Charlier, Jérémy Henri J. ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
State, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Hilger, Jean; Banque et Caisse d'Epargne de l'Etat (BCEE)
External co-authors :
no
Language :
English
Title :
Modeling Smart Contracts Activities: A Tensor based Approach
Publication date :
November 2017
Event name :
2017 Future Technologies Conference (FTC)
Event organizer :
Science and Information (SAI) Conferences
Event place :
Vancouver, Canada
Event date :
from 29-11-2017 to 30-11-2017
Main work title :
Proceedings of 2017 Future Technologies Conference (FTC), 29-30 November 2017, Vancouver, Canada
