  In the current context of Big Data, a multitude of new NoSQL solutions for
storing, managing, and extracting information and patterns from semi-structured
data have been proposed and implemented. These solutions were developed to
relieve the issue of rigid data structures present in relational databases, by
introducing semi-structured and flexible schema design. As current data
generated by different sources and devices, especially from IoT sensors and
actuators, use either XML or JSON format, depending on the application,
database technologies that store and query semi-structured data in XML format
are needed. Thus, Native XML Databases, which were initially designed to
manipulate XML data using standardized querying languages, i.e., XQuery and
XPath, were rebranded as NoSQL Document-Oriented Databases Systems. Currently,
the majority of these solutions have been replaced with the more modern JSON
based Database Management Systems. However, we believe that XML-based solutions
can still deliver performance in executing complex queries on heterogeneous
collections. Unfortunately nowadays, research lacks a clear comparison of the
scalability and performance for database technologies that store and query
documents in XML versus the more modern JSON format. Moreover, to the best of
our knowledge, there are no Big Data-compliant benchmarks for such database
technologies. In this paper, we present a comparison for selected
Document-Oriented Database Systems that either use the XML format to encode
documents, i.e., BaseX, eXist-db, and Sedna, or the JSON format, i.e., MongoDB,
CouchDB, and Couchbase. To underline the performance differences we also
propose a benchmark that uses a heterogeneous complex schema on a large DBLP
corpus.
