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In the current context of Big Data, a multitude of new NoSQL solutions forstoring, managing, and extracting information and patterns from semi-structureddata have been proposed and implemented. These solutions were developed torelieve the issue of rigid data structures present in relational databases, byintroducing semi-structured and flexible schema design. As current datagenerated by different sources and devices, especially from IoT sensors andactuators, use either XML or JSON format, depending on the application,database technologies that store and query semi-structured data in XML formatare needed. Thus, Native XML Databases, which were initially designed tomanipulate XML data using standardized querying languages, i.e., XQuery andXPath, were rebranded as NoSQL Document-Oriented Databases Systems. Currently,the majority of these solutions have been replaced with the more modern JSONbased Database Management Systems. However, we believe that XML-based solutionscan still deliver performance in executing complex queries on heterogeneouscollections. Unfortunately nowadays, research lacks a clear comparison of thescalability and performance for database technologies that store and querydocuments in XML versus the more modern JSON format. Moreover, to the best ofour knowledge, there are no Big Data-compliant benchmarks for such databasetechnologies. In this paper, we present a comparison for selectedDocument-Oriented Database Systems that either use the XML format to encodedocuments, i.e., BaseX, eXist-db, and Sedna, or the JSON format, i.e., MongoDB,CouchDB, and Couchbase. To underline the performance differences we alsopropose a benchmark that uses a heterogeneous complex schema on a large DBLPcorpus.

The Forgotten Document-Oriented Database Management Systems: An Overview and Benchmark of Native XML DODBMSes in Comparison with JSON DODBMSes