Measuring transaction performance based on storage approaches of Native XML database

Many organizations today store their critical business information permanently in XML format. XML data can be managed using: XML-Enabled Database (XED) systems which convert and store XML files in traditional database systems; Native XML Database (NXD) systems which store XML data natively using thr...

Full description

Saved in:
Bibliographic Details
Main Authors: Marjani, Mohsen, Nasaruddin, Fariza Hanum, Gani, Abdullah, Shamshirband, Shahaboddin
Format: Article
Published: Elsevier 2018
Subjects:
Online Access:http://eprints.um.edu.my/21334/
https://doi.org/10.1016/j.measurement.2017.09.028
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many organizations today store their critical business information permanently in XML format. XML data can be managed using: XML-Enabled Database (XED) systems which convert and store XML files in traditional database systems; Native XML Database (NXD) systems which store XML data natively using three main storage technologies – text-based, model-based, and schema-based techniques; and Hybrid Database systems which are comprised of both XML-Enabled and Native XML database systems. NXDs are faster than other database technologies because there is no need to convert the format of the data prior to storage. No performance evaluation has been carried out to compare all three storage strategies, hence, this paper reports on the first attempt to evaluate all three storage strategies by using open source products to measure the response time taken for each of the database basic tasks such as database creation, dataset insertion, and data manipulation. The results of the evaluation show that the schema-based storage strategy: performs 3.5 times faster than the other two storage techniques in data insertion; shows very good performance in query processing on small and large datasets; performs 10.33 times faster than text-based, and 7.5 times faster than model-based storage techniques in query processing of large datasets.