Clientside crosssite scripting protection.docx

Clientside crosssite scripting protection.docx

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Clientsidecrosssitescriptingprotection

在线网络技术

搜索研发

GIS研发

互联网/电子商务

功能设计

linux平台

脚本语言

数据结构和算法设计

OnlineNetwork

SearchR&D

GISR&D

Internet/E-Commerce

FunctionalDesign

linuxplatform

Scriptinglanguage

Datastructureandalgorithmdesign

16,410articlesfoundfor:

pub-date>2002andtak（（（E-Commerce）or（OnlineNetwork）or（GISR&D）or（SearchR&D））and（（FunctionalDesign）or（Scriptinglanguage）or（Datastructure）or（algorithmdesign）or（linuxplatform））andinternet）

Platform-basedproductdesignanddevelopment:

Aknowledge-intensivesupportapproach  

Knowledge-BasedSystems

Thispaperpresentsaknowledge-intensivesupportparadigmforplatform-basedproductfamilydesignanddevelopment.Thefundamentalissuesunderlyingtheproductfamilydesignanddevelopment,includingproductplatformandproductfamilymodeling,productfamilygenerationandevolution,andproductfamilyevaluationforcustomization,arediscussed.Amodule-basedintegrateddesignschemeisproposedwithknowledgesupportforproductfamilyarchitecturemodeling,productplatformestablishment,productfamilygeneration,andproductvariantassessment.Asystematicmethodologyandtherelevanttechnologiesareinvestigatedanddevelopedforknowledgesupportedproductfamilydesignprocess.Thedevelopedinformationandknowledge-modelingframeworkandprototypesystemcanbeusedforplatformproductdesignknowledgecapture,representationandmanagementandofferon-linesupportfordesignersinthedesignprocess.Theissuesandrequirementsrelatedtodevelopingaknowledge-intensivesupportsystemformodularplatform-basedproductfamilydesignarealsoaddressed.

ArticleOutline

1.Introduction

2.Literaturereview

3.Platform-basedproductdesignanddevelopment

4.Productplatformandproductfamilymodeling

4.1.Productfamilyarchitecturemodeling

4.2.Productfamilygenerationandoptimization

4.3.Productfamilyevolutionrepresentation

4.4.Productfamilyevaluationforcustomization

5.Module-basedproductfamilydesignprocess

6.Knowledgesupportframeworkformodularproductfamilydesign

6.1.Knowledgesupportschemeandkeyissues

6.2.Productfamilydesignknowledgemodelingandsupport

6.2.1.Issuesofproductfamilydesignknowledgemodeling

6.2.2.Knowledgemodelingandrepresentationforproductfamilydesign

6.2.3.Knowledgesupportprocessformodularproductfamilydesign

7.Prototypeofknowledge-intensivesupportsystemforproductfamilydesign

8.Summaryandfuturework

9.Disclaimer

References

Cost-basedadmissioncontrolforInternetCommerceQoSenhancement 

ElectronicCommerceResearchandApplications

Inmanye-commercesystems,preservingQualityofService（QoS）iscrucialtokeepacompetitiveedge.PoorQoStranslatesintopoorsystemresourceutilisation,customerdissatisfactionandprofitloss.Inthispaper,acost-basedadmissioncontrol（CBAC）approachisdescribedwhichisanovelapproachtopreserveQoSinInternetCommercesystems.CBACisadynamicmechanismwhichusesacongestioncontroltechniquetomaintainQoSwhilethesystemisonline.Ratherthanrejectingcustomerrequestsinahigh-loadsituation,adiscount-chargemodelwhichissensitivetosystemcurrentloadandnavigationalstructureisusedtoencouragecustomerstopostponetheirrequests.Aschedulingmechanismwithloadforecastingisusedtoscheduleuserrequestsinmorelightlyloadedtimeperiods.ExperimentalresultsshowedthattheuseofCBACathighloadachieveshigherprofit,betterutilisationofsystemresourcesandservicetimescompetitivewiththosewhichareachievableduringlightlyloadedperiods.Throughputissustainedatreasonablelevelsandrequestfailureathighloadisdramaticallyreduced.

ArticleOutline

1.Introduction

2.AnoverviewofCBAC

3.Discount-chargepricingmodel

4.CBAC’snavigationalmodel

5.Customerpostponedrequestscheduling

6.Forecastingsystemload

7.CBAC-specificwebpages

8.Customerbehaviour

9.ECBenchbenchmarkingtool

10.CBACperformanceanalysis

10.1.Servicetime

10.2.CPUutilisation

10.3.Throughputandfailedrequests

10.4.Profit

10.5.CBACoverhead

10.6.CBACloadforecastingeffect

11.Relatedwork

12.Conclusions

References

ActiveRDF:

EmbeddingSemanticWebdataintoobject-orientedlanguages  

WebSemantics:

Science,ServicesandAgentsontheWorldWideWeb

SemanticWebapplicationssharealargeportionofdevelopmenteffortwithdatabase-drivenWebapplications.Existingapproachesfordevelopmentofthesedatabase-drivenapplicationscannotbedirectlyappliedtoSemanticWebdataduetodifferencesintheunderlyingdatamodel.WedevelopamappingapproachthatembedsSemanticWebdataintoobject-orientedlanguagesandtherebyenablesreuseofexistingWebapplicationframeworks.

WeanalysetherelationbetweentheSemanticWebandtheWeb,andsurveythetypicaldataaccesspatternsinSemanticWebapplications.Wediscussthemismatchbetweenobject-orientedprogramminglanguagesandSemanticWebdata,forexampleinthesemanticsofclassmembership,inheritancerelations,andobjectconformancetoschemas.

WepresentActiveRDF,anobject-orientedAPIformanagingRDFdatathatoffersfullmanipulationandqueryingofRDFdata,doesnotrelyonaschemaandfullyconformstoRDF（S）semantics.ActiveRDFcanbeusedwithdifferentRDFdatastores:

adaptershavebeenimplementedtogenericSPARQLendpoints,Sesame,Jena,RedlandandYARSandnewadapterscanbeaddedeasily.WedemonstratetheusageofActiveRDFanditsintegrationwiththepopularRubyonRailsframeworkwhichenablesrapiddevelopmentofSemanticWebapplications.

ArticleOutline

1.Introduction

1.1.Mappingrelationaldata

1.2.Webapplicationframeworks

1.3.Outline

2.Relatedwork

2.1.Object–relationalmappings

2.2.RDFdataaccess

2.3.SemanticWebapplicationdevelopment

3.DevelopingSemanticWebapplications

4.RequirementsforSemanticWebapplicationdevelopment

5.Typicaldataaccessandmanipulationpatterns

6.ProgramminglanguagesforembeddingRDFdata

7.Alayeredarchitectureforprogrammaticaccesstodata

7.1.Adapters

7.2.Federationmanager

7.3.Queryengine

7.4.Objectmanager

8.Evaluation

9.Exampleapplication:

exploringonlinecommunities

9.1.Domain:

socialcommunitiesontheweb

9.2.TheRubyonRailsWebapplicationframework

9.3.ImplementingtheSIOCexplorer

9.3.1.CrawlingSIOCdata

9.3.2.Integratingthedata

9.3.3.Applicationlogic:

socialcontextextraction

9.3.4.FacetednavigationwithBrowseRDF

9.4.ImplementedSemanticWebcapabilities

10.Conclusion

References

Thehybridmodelofneuralnetworksandgeneticalgorithmsforthedesignofcontrolsforinternet-basedsystemsforbusiness-to-consumerelectroniccommerce  

ExpertSystemswithApplications

Researchhighlights

►Ahybridmodelusingneuralnetworksandgeneticalgorithmsisproposed.►Theeffectofsystemenvironmentsoncontrolscanbeestimated.►Theeffectofeachmodeofcontrolsonimplementation（volume）canbeidentified.►Themodelcansuggestthebestsetofvaluesforcontrolstoberecommended.

AsorganizationsbecomeincreasinglydependentonInternet-basedsystemsforbusiness-to-consumerelectroniccommerce（ISB2C）,theissueofISsecuritybecomesincreasinglyimportant.AstheusageofsecuritycontrolsisrelatedtotheimplementationofISB2C,theextentofISB2CcontrolscanbeadjustedinordertoenablethegreatestextentofimplementationofISB2C.ThisstudyintendstoproposeISB2C-NNGA（ISB2C-controlsdesignusingneuralnetworksandgeneticalgorithms）,ahybridoptimizationmodelusingneuralnetworksandgeneticalgorithmsforthedesignofISB2Ccontrols,whichusesback-propagationneuralnetworks（BPN）modelasapredictionofcontrolsusingsystemenvironments,andGAasapatterndirectedsearchmechanismtoestimatetheexponentofindependentvariables（i.e.,ISB2Ccontrols）inmultivariateregressionanalysisofpowermodel.TheeffectofsystemenvironmentsoncontrolscanbeestimatedusingBPNmodelwhichoutperformedlinearregressionanalysisintermsofsquarerootofmeansquarederror.Theeffectofeachmodeofcontrolsonimplementation（volume）canbeidentifiedusingexponentsandstandardizedcoefficientsintheGA-basednonlinearregressionanalysisinISB2C-NNGA.ISB2C-NNGAoutperformedconventionallinearregressionanalysisinpredictionaccuracyintermsoftheaverageRsquareandsumofsquarederror.ISB2CcansuggestthebestsetofvaluesforcontrolstoberecommendedfromseveralcandidatesetsofvaluesforcontrolsbyidentifyingthesetofvaluesforcontrolswhichproducegreatestextentofISB2Cimplementation.TheresultsofstudywillsupportthedesignofISB2Ccontrolseffectively.

ArticleOutline

1.Introduction

2.Theoreticalbackground

2.1.Neuralnetworks

2.2.Geneticalgorithms

2.3.ISB2CControlsforISB2Cimplementation

3.Researchmodel

3.1.Buildaneuralnetworkmodeltoestimatetheeffectofsystemenvironmentsoncontrols

3.2.BuildaGA-basednonlinearregressionmodel

3.3.Determinetheextentofeffectonimplementationbyeachmodeofcontrols

3.4.RecommendthesetofcontrolsformaximumimplementationofISB2Cfromcandidatesetsofcontrols

4.Measuresanddatacollection

5.Results

5.1.EstimationandpredictionofISB2CcontrolsusingBPN

5.2.EstimationandpredictionofISB2CimplementationusingGA-basednonlinearregressionmodel

5.3.Recommendationofcontrols

6.Conclusion

AppendixA

A.1.Systemenvironments

A.2.ISB2CControls

A.3.ISB2CImplementation

References

Cataclysm:

Scalableoverloadpolicingforinternetapplications  

E-fulfillmentandmulti-channeldistribution–Areview  

EuropeanJournalo