English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

As the Extensible Markup Language (XML) rapidly estab- lishes itself as the de facto standard for presenting, stor- ing, and exchanging data on the Internet, large volume of XML data and their supporting facilities start to surface. A fast and accurate selectivity estimation mechanism is of practical importance because selectivity estimation plays a fundamental role in XML query optimization. Recently pro- posed techniques are all based on some forms of structure synopses that could be time-consuming to build and not ef- fective for summarizing complex structure relationships. In this research, we propose an innovative sampling method that can capture the tree structures and intricate relation- ships among nodes in a simple and eectiv e way. The de- rived sample tree is stored as a synopsis for selectivity esti- mation. Extensive experimental results show that, in com- parison with the state-of-the-art structure synopses, specif- ically the TreeSketch and Xseed synopses, our sample tree synopsis applies to a broader range of query types, requires several orders of magnitude less construction time, and gen- erates estimates with considerably better precision for com- plex datasets.

A sampling approach for XML query selectivity estimation