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    • 10. 发明申请
    • MACHINE LEARNING FOR DATABASE MIGRATION SOURCE
    • 机器学习数据库迁移源
    • WO2013149371A1
    • 2013-10-10
    • PCT/CN2012/073455
    • 2012-04-01
    • EMPIRE TECHNOLOGY DEVELOPMENT LLCCAO, JunweiCHEN, Wei
    • CAO, JunweiCHEN, Wei
    • H04L29/06
    • G06N99/005G06F17/303H04L67/1097
    • Technologies are generally provided for maintaining performance level of a database being migrated between different cloud-based service providers employing machine learning. In some examples, data requests submitted to an original data store/database may be submitted to a machine learning-based filter for recording and analysis. Based on the results of the data requests and the filter analyses, new key value structures for a new data store/database may be created. The filter may assign performance scores to the original data requests (made to the original data store) and data requests made to the newly-created key value structures. The filter may then compare the performance scores associated with the created key value structures to each other and to performance scores associated with the original data requests and may select the created key value structures with performance scores that are at least substantially equal to those of the original data requests for the new data store.
    • 通常提供技术来保持使用机器学习的不同的基于云的服务提供商之间迁移的数据库的性能水平。 在一些示例中,提交给原始数据存储/数据库的数据请求可以被提交到基于机器学习的过滤器用于记录和分析。 基于数据请求和过滤器分析的结果,可以创建新的数据存储/数据库的新的键值结构。 过滤器可以将原始数据请求(对原始数据存储区做出)的性能分数和对新创建的键值结构的数据请求分配。 然后,过滤器可以将与所创建的关键值结构相关联的绩效评分与彼此进行比较,以及与原始数据请求相关联的绩效评分,并且可以选择具有至少基本上等于原始数据请求的性能分数的所创建的关键值结构 新数据存储的数据请求。