Handling distributed XML queries over large XML data based on MapReduce framework

With the increase in available extensible markup language (XML) documents, numerous approaches to querying have been proposed in the literature. XPath queries and Twig pattern queries are the two basic approaches, directly affecting the efficiency of XML operations. Distributive manipulation of massive XML data is challenging. This paper aims to develop an efficient distributed XML query processing method using MapReduce, which simultaneously processes several queries on large volumes of XML data. First, we split up a large-scale XML data file into file-splits and put them in a distributed storage system. Then, we present an efficient algorithm to compute different fragments of the document tree using the MapReduce framework in parallel. In order to efficiently handle a large amount of XML data, we built a partition index and used a random access mechanism for specific queries. The experiment results show that our proposed approach is efficient with good scalability.