This dissertation discusses the key technology of XML query pattern matching based on structural joins.
这篇论文主要讨论了基于结构化联接的XML查询模式匹配的相关关键技术。
Nowadays the query optimization technique of XML databases has become a hot research topic in database field, and twig pattern matching is an important problem in XML field.
XML数据库的查询优化技术是当前数据库领域中的一个研究热点，而小枝模式匹配又是其中的一个研究重点。
By making use of the proximity query method in computational geometry, the whole matching query, pattern query, inverse query and outlier detection in time series are studied.
提出了计算几何应用到时间序列挖掘的方法，实现了时间序列全序列匹配查询、模式查询、反向查询和异常检测，查询效率和准确性都有了比较大的提高。
However, the existing segmental semi-Markov model can only detect the matching sequences with approximately equal length to that of the query pattern, i. e., without time scaling.
然而现有的分段半马尔可夫模型只能解决具有近似相同长度的模式检测问题，不允许模式在时间上存在缩放。
In XML database, twig pattern matching is the core of XML query processing and is important for improving the query efficiency.
在XML数据库中，小枝模式查询是XML查询处理的核心操作，它对于提高查询效率是很有意义的。
In XML database, twig pattern matching is the core of XML query processing and is important for improving the query efficiency.
在XML数据库中，小枝模式查询是XML查询处理的核心操作，它对于提高查询效率是很有意义的。
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