简介：Thispaperdescribesanimmersivesystem,called3DIVE,forinteractivevolumedatavisualizationandexplorationinsidetheCAVEvirtualenvironment.Combininginteractivevolumerenderingandvirtualrealityprovidesanaturalimmersiveenvironmentforvolumetricdatavisualization.Moreadvanceddataexplorationoperations,suchasobjectleveldatamanipulation,simulationandanalysis,aresupportedin3DIVEbyseveralnewtechniques.Inparticular,volumeprimitivesandtextureregionsareusedfortherendering,manipulation,andcollisiondetectionofvolumetricobjects;andtheregion-basedrenderingpipelineisintegratedwith3Dimagefilterstoprovideanimage-basedmechanismforinteractivetransferfunctiondesign.ThesystemhasbeenrecentlyreleasedaspublicdomainsoftwareforCAVE/ImmersaDeskusers,andiscurrentlybeingactivelyusedbyvariousscientificandbiomedicalvisualizationprojects.

简介：Withseveralricegenomeprojectsapproachingcompletiongeneprediction/findingbycomputeralgorithmshasbecomeanurgenttask.Twotestsetswereconstructedbymappingthenewlypublished28,469full-lengthKOMEricecDNAtotheRGPBACclonesequencesofOryzasativassp.japonica:asingle-genesetof550sequencesandamulti-genesetof62sequenceswith271genes.Thesedatasetswereusedtoevaluatefiveabinitiogenepredictionprograms:RiceHMM,GlimmerR,GeneMark,FGENSHandBGF.Thepredictionswerecomparedonnucleotide,exonandwholegenestructurelevelsusingcommonlyacceptedmeasuresandseveralnewmeasures.Thetestresultsshowaprogressinperformanceinchronologicalorder.Atthesametimecomplementarityoftheprogramshintsonthepossibilityoffurtherimprovementandonthefeasibilityofreachingbetterperformancebycombiningseveralgene-finders.

简介：Asusersincreasinglybefriendothersandinteractonlineviatheirsocialmediaaccounts,onlinesocialnetworks(OSNs)areexpandingrapidly.Confrontedwiththebigdatageneratedbyusers,itisimperativethatdatastoragebedistributed,scalable,andcost-efficient.Yetoneofthemostsignificantchallengesaboutthistopicisdetermininghowtominimizethecostwithoutdeterioratingsystemperformance.Althoughmanystoragesystemsusethedistributedkeyvaluestore,itcannotbedirectlyappliedtoOSNstoragesystems.Andbecauseusers'dataarehighlycorrelated,hashstorageleadstofrequentinter-servercommunications,andthehighinter-servertrafficcostsdecreasetheOSNstoragesystem'sscalability.Previousstudiesproposedconductingnetworkpartitioninganddatareplicationbasedonsocialgraphs.However,datareplicationincreasesstoragecostsandimpactstrafficcosts.Here,weconsiderhowtominimizecostsfromtheperspectiveofdatastorage,bycombiningpartitioningandreplication.Ourcost-efficientdatastorageapproachsupportsscalableOSNstoragesystems.Theproposedapproachco-locatesfrequentlyinteractiveuserstogetherbyconductingpartitioningandreplicationsimultaneouslywhilemeetingload-balancingconstraints.Extensiveexperimentsareundertakenontworeal-worldtraces,andtheresultsshowthatourapproachachieveslowercostcomparedwithstate-of-the-artapproaches.ThusweconcludethatourapproachenableseconomicandscalableOSNdatastorage.

简介：这是I-OData针对白领丽人推出的移动硬盘，外形精巧，有粉红色．黑色．珍珠白三种颜色．容量有250GB。320GB、500GB供选择，一条USB线巧妙地缠绕在移动硬盘的外壳上。

简介：数据存取延期是在利用高端计算(HEC)用机器制造的电流的一个主要瓶颈。在数据在中央处理器前被取的地方，预取为它要求，被看作了掩盖数据存取延期的一个有效答案。然而，在一个计算处理器开始预取指令的地方，当前的开始顾客的预取策略有许多限制。他们不与复杂、非连续的数据存取模式为应用工作很好。当技术进展时，继续增加在计算和数据存取性能之间的差距，交换计算力量因为减少数据存取延期成为了一种自然选择。在这篇论文，我们在场基于服务者的数据推接近并且讨论它的联系实现机制。在服务者推建筑学，打电话给数据推服务者(DPS)的一个奉献服务者开始，专业版活跃地及时更近把数据推到顾客。问题，象，取取的数据被学习，并且怎么推的那样。SimpleScalar模拟器与为到测试DPS的另一个处理器的推数据基于预取的一台奉献预取的引擎被修改。模拟结果证明那L1高速缓存故障率能被多达97%减少(71%平均)在为有高高速缓存故障率的说明CPU2000基准的一台超级标量处理机上。电子增补材料这篇文章(doi:10.1007/s11390-007-9090-y)的联机版本包含增补材料，它对授权用户可得到。

简介：Thegeneralconceptofdatacompressionconsistsinremovingtheredundancyexistingindatatofindamorecompactrepresentation.Thispaperisconcernedwithanewmethodofcompressionusingthesecondgenerationwaveletsbasedontheliftingscheme,whichisasimplebutpowerfulwaveletconstructionmethod.Ithasbeenprovedbyitssuccessfulapplicationtoareal-timemonitoringsystemoflargehydraulicmachinesthatitisapromisingcompressionmethod.

简介：Adatastreamisamassiveunboundedsequenceofdataelementscontinuouslygeneratedatarapidrate.Duetothisreason,mostalgorithmsfordatastreamssacrificethecorrectnessoftheirresultsforfastprocessingtime.Theprocessingtimeisgreatlyinfluencedbytheamountofinformationthatshouldbemaintained.Thisissuebecomesmoreseriousinfindingfrequentitemsetsorfrequencycountingoveranonlinetransactionaldatastreamsincetherecanbealargenumberofitemsetstobemonitored.WehaveproposedamethodcalledtheestDecmethodforfindingfrequentitemsetsoveranonlinedatastream.Inordertoreducethenumberofmonitoreditemsetsinthismethod,monitoringthecountofanitemsetisdelayeduntilitssupportislargeenoughtobecomeafrequentitemsetinthenearfuture.Forthispurpose,thecountofanitemsetshouldbeestimated.Consequently,howtoestimatethecountofanitemsetisacriticalissueinminimizingmemoryusageaswellasprocessingtime.Inthispaper,theeffectsofvariouscountestimationmethodsforfindingfrequentitemsetsareanalyzedintermsofminingaccuracy,memoryusageandprocessingtime.

简介：服务器的差的精力比例为现代数据中心的低精力效率被看作主要来源。我们发现一个应用程序的不同资源配置导致类似的性能，但是有不同精力消费。当表演等价物资源配置(PERC)，和它的性能范围被称为相等的区域，我们叫这现象(嗯)。基于PERC，为改进精力效率的一个基本想法是为每应用从PERC选择最有效的配置。然而，当几千个应用程序在围住资源的服务器上同时被运行时，获得最佳的答案不能支持每个应用程序。这里，我们建议一个启发式的计划，CPicker，基于基因编程到改进服务者的精力效率。加快集中，CPicker由首先从有高精力的区域选择配置初始化一张高质量的人口变化。试验CPicker与神谕盒子相比与贪婪途径，和不到4%效率损失相比在17%精力效率改进上面获得的表演。

简介：Denovosequencingisoneofthemostpromisingproteomicstechniquesforidentificationofproteinposttranslationmodifications(PTMs)instudyingproteinregulationsandfunctions.WehavedevelopedacomputertoolPRIMEforidentificationofbandyionsintandemmassspectra,akeychallengingproblemindenovosequencing.PRIMEutilizesafeaturethationsofthesameanddifferenttypesfollowdifferentmass-differencedistributionstoseparatebfromyionscorrectly.Wehaveformulatedtheproblemasagraphpartitionproblem.Alinearinteger-programmingalgorithmhasbeenimplementedtosolvethegraphpartitionproblemrigorouslyandefficiently.TheperformanceofPRIMEhasbeendemonstratedonalargeamountofsimulatedtandemmassspectraderivedfromYeastgenomeanditspowerofdetectingPTMshasbeentestedon216simulatedphosphopeptides.

简介：参量的软件努力评价模型通常由仅仅一个单个数学关系式组成。与从异构的工程包含数据的软件仓库的来临，模型的这些类型受不了差的调整和预兆的精确性。减轻这个问题的一个可能的方法是通过根据不同参数历史的工程数据集划分成称为分区的潜水艇数据集获得的一套数学方程的使用。接着，分区被划分成为更精确的模型用作一个工具的簇。在这篇论文，我们用一个公开可得到的仓库通过案例研究描述如此的途径的过程，工具和结果，ISBSG。没有让评价处理使用一个单个评价模型的更多建筑群，结果作为存在单个表示的模型的延期建议这种技术的足够。支持这个过程的一个工具也被介绍。电子增补材料这篇文章(doi:10.1007/s11390-007-9043-5)的联机版本contatins增补材料，它对授权用户可得到。

简介：Inthispaper,anewmethod,namedasL-treematch,ispresentedforextractingdatafromcomplexdatasources.Firstly,basedondataextractionlogicpresentedinthiswork,anewdataextractionmodelisconstructedinwhichmodelcomponentsarestructurallycorrelatedviaageneralizedtemplate.Secondly,adatabase-populatingmechanismisbuilt,alongwithsomeobject-manipulatingoperationsneededforflexibledatabasedesign,tosupportdataextractionfromhugetextstream.Thirdly,top-downandbottom-upstrategiesarecombinedtodesignanewextractionalgorithmthatcanextractdatafromdatasourceswithoptional,unordered,nested,and/ornoisycomponents.Lastly,thismethodisappliedtoextractaccuratedatafrombiologicaldocumentsamountingto100GBforthefirstonlineintegratedbiologicaldatawarehouseofChina.

简介：Withthegrowingpopularityofcloud-baseddatacenternetworks(DCNs),taskresourceallocationhasbecomemoreandmoreimportanttotheefficientuseofresourceinDCNs.Thispaperconsidersprovisioningthemaximumadmissibleload(MAL)ofvirtualmachines(VMs)inphysicalmachines(PMs)withunderlyingtree-structuredDCNsusingthehosemodelforcommunication.Thelimitationofstaticloaddistributionisthatitassignstaskstonodesinaonce-and-for-allmanner,andthusrequiresaprioriknowledgeofprogrambehavior.Toavoidloadredistributionduringruntimewhentheloadgrows,weintroducemaximumelasticityscheduling,whichhasthemaximumgrowthpotentialsubjecttothenodeandlinkcapacities.Thispaperaimstofindtheschedulewiththemaximumelasticityacrossnodesandlinks.Wefirstproposeadistributedlinearsolutionbasedonmessagepassing,andwediscussseveralpropertiesandextensionsofthemodel.Basedontheassumptionsandconclusions,weextendittothemultiplepathscasewithafattreeDCN,anddiscusstheoptimalsolutionforcomputingtheMALwithbothcomputationandcommunicationconstraints.Afterthat,wepresenttheprovisionschemewiththemaximumelasticityfortheVMs,whichcomeswithprovableoptimalityguaranteeforafixedflowschedulingstrategyinafattreeDCN.Weconducttheevaluationsonourtestbedandpresentvarioussimulationresultsbycomparingtheproposedmaximumelasticschedulingschemeswithothermethods.Extensivesimulationsvalidatetheeffectivenessoftheproposedpolicies,andtheresultsareshownfromdifferentperspectivestoprovidesolutionsbasedonourresearch.

简介：Cloudcomputingisatechnologythatprovidesuserswithalargestoragespaceandanenormouscomputingpower.However,theoutsourceddataareoftensensitiveandconfidential,andhencemustbeencryptedbeforebeingoutsourced.Consequently,classicalsearchapproacheshavebecomeobsoleteandnewapproachesthatarecompatiblewithencrypteddatahavebecomeanecessity.Forprivacyreasons,mostoftheseapproachesarebasedonthevectormodelwhichisatimeconsumingprocesssincetheentireindexmustbeloadedandexploitedduringthesearchprocessgiventhatthequeryvectormustbecomparedwitheachdocumentvector.Tosolvethisproblem,weproposeanewmethodforconstructingasecureinvertedindexusingtwokeytechniques,homomorphicencryptionandthedummydocumentstechnique.However,1)homomorphicencryptiongeneratesverylargeciphertextswhicharethousandsoftimeslargerthantheircorrespondingplaintexts,and2)thedummydocumentstechniquethatenhancestheindexsecurityproduceslotsoffalsepositivesinthesearchresults.Theproposedapproachexploitstheadvantagesofthesetwotechniquesbyproposingtwomethodscalledthecompressedtableofencryptedscoresandthedoublescoreformula.Moreover,weexploitasecondsecureinvertedindexinordertomanagetheusers'accessrightstothedata.Finally,inordertovalidateourapproach,weperformedanexperimentalstudyusingadatacollectionofonemilliondocuments.Theexperimentsshowthatourapproachismanytimesfasterthananyotherapproachbasedonthevectormodel.

简介：Whilecloud-basedBPM(BusinessProcessManagement)showspotentialsofinherentscalabilityandexpenditurereduction,suchissuesasuserautonomy,privacyprotectionandefficiencyhavepoppedupasmajorconcerns.Usersmayhavetheirownrudimentaryorevenfull-edgedBPMsystems,whichmaybeembodiedbylocalEAIsystems,attheirend,butstillintendtomakeuseofcloud-sideinfrastructureservicesandBPMcapabilities,whichmayappearasPaaS(Platform-as-a-Service)services,atthesametime.Awholebusinessprocessmaycontainanumberofnon-compute-intensiveactivities,forwhichcloudcomputingisover-provision.Moreover,someusersfeardataleakageandlossofprivacyiftheirsensitivedataisprocessedinthecloud.Thispaperproposesandanalyzesanovelarchitectureofcloud-basedBPM,whichsupportsuser-enddistributionofnon-compute-intensiveactivitiesandsensitivedata.Anapproachtooptimaldistributionofactivitiesanddataforsyntheticallyutilizingbothuser-endandcloud-sideresourcesisdiscussed.Experimentalresultsshowthatwiththehelpofsuitabledistributionschemes,dataprivacycanbesatisfactorilyprotected,andresourcesonbothsidescanbeutilizedatlowercost.

简介：尽管很少调查检查了这个对象，热数据鉴定为许多应用是关键的。所有存在研究几乎专门集中于频率。然而，有效地识别热数据就崭新和频率而言同等地要求。而且，以前的研究在数据块水平做热数据决定。因为它的随机的存取完成与它的顺序的存取可比较的性能，如此的一个有细密纹理的决定为基于闪光的存储特别地适合很好。然而，硬盘驱动器(HDD)有在顺序、随机的存取之间的重要性能不同。不同于基于闪光的存储，因此利用不对称的HDD存取性能要求做一个纹理粗糙的决定。这份报纸建议采用多重花蕾过滤器高效地描绘崭新以及频率的一个新奇热数据鉴定计划。因而，它不仅消费50%更少的存储器和多达58%不太计算的开销，而且与一个最先进的计划相比降低假鉴定率直到65%。而且，我们把计划用于一种下一代HDD技术，即，Shingled磁性的记录(SMR)，验证它的有效性。为这，我们设计新热数据鉴定有一个纹理粗糙的决定的基于的SMR开车。实验表明精确热数据鉴定的重要性和好处，从而在多达42%改进建议SMR驱动器性能。