Abstract
Extensible Markup Language (XML) has become a significant technology for transferring data through the world of the Internet. XML labelling schemes are an essential technique used to handle XML data effectively. Labelling XML data is performed by assigning labels to all nodes in that XML document. CLS labelling scheme is a hybrid labelling scheme that was developed to address some limitations of indexing XML data. Moreover, datasets are used to test XML labelling schemes. There are many XML datasets available nowadays. Some of them are from real life datasets and others are from artificial datasets. These datasets and benchmarks are used for testing the XML labelling schemes. This paper discusses and considers these datasets and benchmarks and their specifications in order to determine the most appropriate one for testing the CLS labelling scheme. This research found out that the XMark benchmark is the most appropriate choice for the testing performance of the CLS labelling scheme.
Highlights
XML was recommended in 1998 by the World Wide Web Consortium (W3C)
Various existing XML datasets and benchmarks are used for performance testing of XML labelling schemes[11]
Some of them are from real life datasets and others are from artificial datasets
Summary
XML was recommended in 1998 by the World Wide Web Consortium (W3C). XML has become the dominant technology for transferring data across the internet. Indexing XML is a very important technique that used to improve XML data queries. The efficiency of the performance of any query in a database is based on indexing [1, 2]. Labelling XML data is the technique used to index XML data efficiently. An effective XML labelling scheme should give efficient query performance. These XML labelling are tested by using the XML benchmarks and XML datasets[10]. Various existing XML datasets and benchmarks are used for performance testing of XML labelling schemes[11]. Some of them are from real life datasets and others are from artificial datasets. These two types of datasets are Article History : Received 03 June 2021 - Received in revised form 07 July 2021 - Accepted 15 July 2021
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