关键词: 可扩展标记语言（XML）, 结构连接, MapReduce

Abstract: Extensible markup language (XML) has become the defacto standard of data representation and data exchange on Web. Hadoop is a typical framework for cloud computing and big data processing, thus making a study on XML query processing based on MapReduce is necessary. In order to implement the XML query processing based on MapReduce, this paper proposes three different encoding algorithms such as interval encoding, prefix encoding and hierarchy encoding, and designs the corresponding structural join algorithms based on MapReduce to support XML queries. This paper sets up a cost model for the query processing, and proposes a cost-based approach to determine the optimal execution tree. In the end, the XML query processing experiments are made, the experimental results show that relative to other two XML encoding schemes, the query processing based on interval encoding has a higher query performance. And the cost-based optimal approach is effective and further improves the performance of XML query processing.

Key words: extensible markup language (XML), structural join, MapReduce