事务数据库的英文

• transactional databases

• "事务"英文翻译    affair
• "数据库"英文翻译    data bank; data base; data p ...
• "任务数据库" 英文翻译 :    tdb task database
• "业务数据库" 英文翻译 :    service database
• "事务数据" 英文翻译 :    transaction data
• "事务数据集" 英文翻译 :    transaction data set
• "事务数据处理" 英文翻译 :    administrative date processing; transaction data processing
• "财会与税务数据库" 英文翻译 :    accounting & tax
• "欧洲商务数据库" 英文翻译 :    european business
• "商业服务数据库" 英文翻译 :    bsdb business service database
• "信息服务数据库" 英文翻译 :    responsive database services limited
• "亚洲商务数据库" 英文翻译 :    asian business
• "呼叫管理服务数据库" 英文翻译 :    cmsdb call management service database
• "事务处理数据库" 英文翻译 :    transactional data base
• "并行数据库事务处理" 英文翻译 :    concurrent database transaction
• "分布式数据库事务处理" 英文翻译 :    distributed database transaction
• "律师和法律事务所数据库" 英文翻译 :    law firm database
• "数据库；数据库" 英文翻译 :    data bank
• "财务数据" 英文翻译 :    financial data
• "服务数据" 英文翻译 :    service data
• "服务数据块" 英文翻译 :    service data unit
• "内务数据" 英文翻译 :    housekeeping data
• "任务数据" 英文翻译 :    task data
• "业务数据" 英文翻译 :    business data
• "业务数据点" 英文翻译 :    service data point sdp

• A transaction database will have frequent checkpoints
  而事务数据库则具有经常发生的检查点。
• Association rule mining techniques discover unordered correlations between items found in a database of transactions
  而关联规则挖掘则是发现事务数据库中数据项之间的相关性。
• The maximum forward path method can transform the data into transactional databases , which are appropriated for mining
  运用最大向前引用方法对用户会话进行分割，得到用户的事务数据库。
• To handle the timesequenceing between iii transactions , we define interval base concept lattice as an extension to classical concept lattice
  为了对事务数据库中不同事务之间的时序关系进行处理，我们定义了间隔基准概念格作为对经典概念格的一种扩展。
• However , transaction database is sometimes not a simple set of transactions , there may exist some relation ( such as timesequencing ) between different transactions
  然而，事务数据库有时并不只是事务的简单集合，不同事物之间可能存在某种关系（如时序关系） 。
• 2 . improve the apriori algorithm for mining the single - dimensional , single - level , boolean association rules . design and implement the algorithm which can mining hybrid - dimensional association rules within multi - dimensional transaction database
  对单维、单层、布尔型关联规则挖掘算法? ? apriori算法进行改进，设计并实现了基于多维事务数据库的混合维关联规则挖掘算法。
• This paper presents the directed itemsets graph to store the information of frequent itemsets of transaction databases , and puts forward the trifurcate linked list storage structure of directed itemsets graph , and provides the mining algorithm of frequent closed itemsets based on directed itemsets graph
  摘要利用了有向项集图来存储事务数据库中有关频繁项集的信息，提出了有向项集图的三叉链表式存储结构和在于有向项集图的频繁闭项集挖掘算法。
• At the present time , the mining of association rules in the databases of transactions is mostly applied to the merchandise field , for example , the control of the sales in super markets , etc . the present applications of association rules mining in industrial process are not profound
  在事务数据库中挖掘关联规则是数据挖掘领域中一个非常重要的研究课题，目前其实际应用主要集中在零售业，比如超级市场的销售管理等，在流程工业领域中的实际应用尚不多见。
• An efficient algorithm qais is proposed that uses the efficient method to reduce database access activity , and present a novel algorithm aiu based on this algorithm , it is fit for mining association rules and incremental updating . it is especially effective in vldb , mining long patterns , and high support . the perfermance of qais / aiu is verified on the basis of synthetic data , experiments show that the proposed algorithm can mine association rules more efficiently by not generating candidate itemsets and reducing the redundancy of frequent itemsets while generating association rules
  然而目前大多数挖掘关联规则的算法往往必需多次扫描事务数据库才能达到要求的目标，这样重复性的数据库存取动作将会导致过多的执行时问浪费在i o动作上，另外在频繁集生成规则时现有的算法没有考虑到规则的大量冗余，为了解决这个问题本文提出了一个不需要生成候选集同时有效的去除生成规则时产生大量冗余的算法qais ，来提高关联规则生成的速度，并且在此算法基础上提出了新颖的关联规则增量更新算法aiu ，通过应用合成数据验证了qais aiu算法的有效性，由试验结果来看这个方法确实能更有效且准确地获得事务数据库的关联规则，尤其适合挖掘超大数据库中高支持度下长模式的挖掘。
• So in our system the path - analysis methods include the following steps : 1 ) to identify the browsing process each time ; 2 ) to identify all window sessions in one browsing process ; 3 ) to construct the extended - directed - tree of every . window session to reflect the users " browsing behavior in a window session ; 4 ) to combine all extended - directed - trees to form an extended - directed - forest ; 5 ) to identify " transaction and produce the database of transaction ; 6 ) to mine the frequent path referenced in transaction database , then the users " profile about their browsing behavior is produced
  本系统中的路径挖掘包括了如下几个步骤： 1 )识别用户每一次浏览过程； 2 )识别一次浏览过程中所有的窗口会话； 3 )为每个窗口会话构造反映用户浏览行为的扩展有向树； 4 )合并扩展有向树，得到反映用户本次浏览过程全局行为的扩展有向树林； 5 )事务识别，产生事务数据库； 6 )在事务数据库上挖掘频繁引用路径，由此产生或更新基于用户的浏览行为习惯的模型p _ h 。