简介：Asystematic,efficientcompilationmethodforqueryevaluationofDeductiveDatabases(DeDB)isproposedinthispaper.Inordertoeliminateredundancyandtominimizethepotentiallyrelevantfacts,whicharetwokeyissuestotheefficiencyofaDeDB,thecompilationprocessisdecomposedintotwophases.Thefirstisthepre-compilationphase,whichisresponsiblefortheminimizationofthepotentiallyrelevantfacts.Thesecond,whichwerefertoasthegeneralcompilationphase,isresponsiblefortheeliminationofredundancy.Therule/goalgraphdevisedbyJ.D.Ullmanisappropriatelyextendedandusedasauniformformalism.Twogeneralalgorithmscorrespondingtothetwophasesrespectivelyaredescribedintuitivelyandformally.

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简介：Aconceptualleveldatabaselanguagefortheentityrelationship(ER)modelimplicitlycontainsintegritiesbasictoERconceptsandspecialretrievalsemanticsforinheritancesofattributesandrelationships.Prolog,whichbelongstothelogicalandphysicallevel,cannotbeusedasafoundationtodirectlydefinethedatabaselanguage.ItisshownhowPrologcanbeenhancedtounderstandtheconceptsofentities,relationships,attributesandis-arelationships.TheenhancedPrologisthenusedasafoundationtodefinethesemanticsofadatabasequerylanguagefortheERmodel.Thethreebasicfunctionsofmodelspecification,updatesandretrievalsaredefined.

简介：Theconstructionofoceanographicontologiesisfundamentaltothe'digitalocean'.Therefore,onthebasisofintroductionofnewconceptofoceanographicontology,anoceanographicontology-basedspatialknowledgequery(OOBSKQ)methodwasproposedanddeveloped.Becausethemethodusesanaturallanguagetodescribequeryconditionsandthequeryresultishighlyintegratedknowledge,itcanprovideuserswithdirectanswerswhilehidingthecomplicatedcomputationandreasoningprocesses,andachievesintelligent,automaticoceanographicspatialinformationqueryonthelevelofknowledgeandsemantics.Acasestudyofresourceandenvironmentalapplicationinbayhasshowntheimplementationprocessofthemethodanditsfeasibilityandusefulness.

简介：Approximatequeryprocessinghasemergedasanapproachtodealingwiththehugedatavolumeandcomplexqueriesintheenvironmentofdatawarehouse.Inthispaper,wepresentanovelmethodthatprovidesapproximateanswerstoOLAPqueries.Ourmethodisbasedonbuildingacompressed(approximate)datacubebyaclusteringtechniqueandusingthiscompresseddatacubetoprovideanswerstoqueriesdirectly,soitimprovestheperformanceofthequeries.WealsoprovidethealgorithmoftheOLAPqueriesandtheconfidenceintervalsofqueryresults.AnextensiveexperimentalstudywiththeOLAPcouncilbenchmarkshowstheeffectivenessandscalabilityofourcluster-basedapproachcomparedtosampling.

简介：Inmanyapplicationsanddomains,temporalconstraintsbetweenactions,andtheirprobabilitiesplayanimportantrole.Weproposethefirstapproachintheliteraturecopingwithprobabilisticquantitativeconstraints.Toachievesuchachallenginggoal,weextendthewidelyusedsimpletemporalproblem(STP)frameworktoconsiderprobabilities.Specifically,weproposei)aformalrepresentationofprobabilisticquantitativeconstraints,ii)analgorithm,basedontheoperationsofintersectionandcomposition,forthepropagationofsuchtemporalconstraints,andiii)facilitiestosupportqueryansweringonasetofsuchconstraints.Asaresult,weprovideuserswiththefirsthomogeneousmethodsupportingthetreatment(representing,reasoning,andquerying)ofprobabilisticquantitativeconstraints,asrequiredbymanyapplicationsanddomains.

简介：这篇论文提供复合条件质问(CCQ)和基于空格从踪迹代数学导出的踪迹信息使用要求引导的一个形式化方法的应用程序问题的一个形式化的定义。与解决CCQ的应用程序问题的形式化的进程，CCQ的申请的正式要求说明被给，一形式化，到正式要求说明的要求引导的结果的自动印射被执行，软件系统模型和应用程序代码被开发。通过住房信息上的全面质问的一个样品应用程序，可行性形式化，为CCQ的应用程序问题的自动软件开发被证明。结果关于形式化和自动软件开发为另外的问题有重要含意。

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简介：反碰撞的技术是在收音机频率鉴定(RFID)的一个关键点系统。避免数据碰撞，有二条途径：阿罗哈基于算法，二进制树(BT)基于算法。然而，这些不能完全解决碰撞问题，特别当标签数量大，标签标志长时。在这篇文章，我们在场一个多分支询问树(MBQT)协议基于平衡的不完全的块设计(BIBD)，代码，和使用16位向量作为RFID的询问前缀符号源于BIBD阅读器。与常规反碰撞算法相比，理论上的分析和模拟证明建议协议改进鉴定效率。

简介：用户声誉的研究对于互联网金融和电子商务的健康发展具有重要意义,是在线用户行为分析中一个重要的研究方向。在线用户评分系统中研究学者提出了许多声誉度量算法,然而不同方法度量用户声誉的思想和角度是不同的。为了在海量数据中对用户声誉有一个总体的认识,提出一种基于SkylineQuery的高声誉用户识别方法。将已有的几种声誉度量方法进行分类,综合选取代表性的算法得到的用户声誉用Skyline查询方法找到的集合Skyline中不被其他用户所支配的用户,即为高声誉用户。同时分析不同时间段上得到的集合Skyline中高声誉用户的规律。本文综合多种声誉度量方法从定性角度对声誉进行应用研究,拓宽了用户声誉研究的广度。

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简介：AbstractObjective:Medical data mining and sharing is an important process in E-Health applications. However, because these data consist of a large amount of personal private information of patients, there is the risk of privacy disclosure when sharing and mining. Therefore, ensuring the security of medical big data in the process of publishing, sharing, and mining has become the focus of current research. The objective of our study is to design a framework based on a differential privacy protection mechanism to ensure the secure sharing of medical data. We developed a privacy protection query language (PQL) that integrates multiple data mining methods and provides a secure sharing function.Methods:This study is mainly performed in Xuzhou Medical University, China and designs three sub-modules: a parsing module, mining module, and noising module. Each module encapsulates different computing methods, such as a composite parser and a noise theory. In the PQL framework, we apply the differential privacy theory to the results of the computing between modules to guarantee the security of various mining algorithms. These computing devices operate independently, but the mining results depend on their cooperation. In addition, PQL is encapsulated in MNSSp3 that is a data mining and security sharing platform and the data comes from public data sets, such as UCBI. The public data set (NCBI database) was used as the experimental data, and the data collection time was January 2020.Results:We designed and developed a query language that provides functions for medical data mining, sharing, and privacy preservation. We theoretically proved the performance of the PQL framework. The experimental results show that the PQL framework can ensure the security of each mining result and the availability of the output results is above 97%.Conclusion:Our framework enables medical data providers to securely share health data or treatment data and develops a usable query language, based on a differential privacy mechanism, that enables researchers to mine information securely using data mining algorithms.

简介：目的介绍MicrosoftQuery技术在医院统计工作中的应用。方法利用MicrosoftQuery自定义查询条件，从医院数据库提取数据产出到Excel。结果无须编程，即可及时、准确、全面、动态的获取数据库中的数据，结合Excel进行分析，能更快、更好的提供有价值的统计信息。结论MicrosoftQuery技术值得在医院统计工作中得到进一步推广应用。

简介：一线性(q，，，m(n))局部地可译码的代码(LDC)C：\mathbbF{\mathbbF}n\mathbbF{\mathbbF}m(n)是从向量空间\mathbbF的线性转变{\mathbbF}n到空间\mathbbF{\mathbbF}每消息标志xi能与概率为被恢复的m(n)至少\frac1|\mathbbF|+e\frac{1}{{\left|\mathbb{F}\right|}}从由查询仅仅q的一个使随机化的算法的C(x)的+\varepsilonC放(x)，就算直到C的m(n)位置(x)被贿赂。在Dvir的一个最近的工作，为线性LDC降低界限的作者表演能为算术电路暗示更低的界限。他建议那证明界限更低因为在建筑群或真实的地上的LDC是为接近他的之一的一个好起点推测。我们的主要结果是m(n)=(n2)为在任何东西上的线性3质问LDC的更低的界限，可能无限，地。常数在(

简介：在这篇论文，我们建议一个新关系纲要(R纲要)到XML纲要翻译算法，VQT，它分析价值集的势和用户质问模式并且由在用户询问使用在列和equi加入特征之间的外国关键限制的集的势性质提取含蓄的参考正直。VQT算法罐头把提取暗示参考正直关系信息用于R纲要并且当期末考试结果，创造一个XML纲要。因此，VQT算法阻止R纲要是不正确地折合当翻译代表结果，XML纲要，和它丰富地并且有力地由创造一个XML纲要在R纲要表示所有信息XMLDTD。