The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Efficient Storage and Parallel Query of Massive XML Data in Hadoop
Abstract
In order to solve the problem of storage and query for massive XML data, a method of efficient storage and parallel query for a massive volume of XML data with Hadoop is proposed. This method can store massive XML data in Hadoop and the massive XML data is divided into many XML data blocks and loaded on HDFS. The parallel query method of massive XML data is proposed, which uses parallel XPath queries based on multiple predicate selection, and the results of parallel query can satisfy the requirement of query given by the user. In this chapter, the map logic algorithm and the reduce logic algorithm based on parallel XPath queries based using MapReduce programming model are proposed, and the parallel query processing of massive XML data is realized. In addition, the method of MapReduce query optimization based on multiple predicate selection is proposed to reduce the data transfer volume of the system and improve the performance of the system. Finally, the effectiveness of the proposed method is verified by experiment.
Related Content
|
Sreerakuvandana Sreerakuvandana, Princy Pappachan, Varsha Arya.
© 2024.
24 pages.
|
|
Sandfreni, Ritika Bansal.
© 2024.
57 pages.
|
|
Ankita Manohar Walawalkar, Massoud Moslehpour, Thanaporn Phattanaviroj, Suman Kumar.
© 2024.
33 pages.
|
|
Akshat Gaurav, Brij B. Gupta, Arcangelo Castiglione.
© 2024.
30 pages.
|
|
Gerry Firmansyah, Shavi Bansal, Ankita Manohar Walawalkar, Suman Kumar, Sourasis Chattopadhyay.
© 2024.
33 pages.
|
|
Princy Pappachan, Massoud Moslehpour, Ritika Bansal, Mosiur Rahaman.
© 2024.
34 pages.
|
|
Akshat Gaurav, Brij B. Gupta, Jinsong Wu, Priyanka Chaurasia.
© 2024.
27 pages.
|
|
|