A framework for ABFT techniques in the design of fault-tolerant computing systems

Abstract

We present a framework for algorithm-based fault tolerance (ABFT) methods in the design of fault tolerant computing systems. The ABFT error detection technique relies on the comparison of parity values computed in two ways. The parallel processing of input parity values produce output parity values comparable with parity values regenerated from the original processed outputs. Number data processing errors are detected by comparing parity values associated with a convolution code. This article proposes a new computing paradigm to provide fault tolerance for numerical algorithms. The data processing system is protected through parity values defined by a high-rate real convolution code. Parity comparisons provide error detection, while output data correction is affected by a decoding method that includes both round-off error and computer-induced errors. To use ABFT methods efficiently, a systematic form is desirable. A class of burst-correcting convolution codes will be investigated. The purpose is to describe new protection techniques that are easily combined with data processing methods, leading to more effective fault tolerance.

An Efficient Predictive Model for Probability of Genetic Diseases Transmission

“…The prerequisite for the association rules contains medical measurements and risk factors, while its consequences are degree of severity of the disease is in one or more arteries. Predictive rules by the association rules mining are more frequent and enjoy higher reliability than the predictive rules induced by the decision tree [38][39][40][41].…”

An Approach to Improve Generation of Association Rules in Order to Be Used in Recommenders

“…Oneimportantproblemindataminingisthediscoveryoffrequentitemsetsinatransactionaldatabase (Leung,Chan,&Chung,2006).Frequentitemsetminingisatraditionalandimportantproblemindata mining.Anitemsetisfrequentifitssupportisnotlessthanaminimumsupportspecifiedbyusers. Traditionalfrequentitemsetminingapproacheshavemainlyconsideredtheproblemofminingstatic transactiondatabases.Inthesemethods,transactionsarestoredinsecondarystoragesothatmultiple scansoverthedatacanbeperformed.Frequentpatterns,suchasfrequentitemsets,substructures, sequencesterm-sets,phrasesets,andsubgraphs,generallyexistinreal-worlddatabases.Identifying frequentitemsetsisoneofthemostimportantissuesfacedbytheknowledgediscoveryanddata miningcommunity.Frequentitemsetminingplaysanimportantroleinseveraldataminingfields asassociationrules (Chandak,Girase,&Mukhopadhyay,2015;Mousavietal.,2017;(Bimonteet al,2017;Hamidietal.,2016),warehousing (Daraeietal.,2016;Hamidi,2011Hamidi, ,2009Hamidi, ,2010Hamidi, , 2011Hamidi, ,2017,correlations,clusteringofhigh-dimensionalbiologicaldata,andclassification (Linet al,2002;Hashemzadehetal.,2016;Mohammadietal.,2005).…”