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

Histograms have been widely used for selectivity estimation in query optimization, as well as for fast approximate query answering in many data mining, OLAP, and data visualization applications. This paper presents a new type of multi-dimensional histogram, the multi-dimensional VI histogram. Unlike other types of multi-dimensional histograms, which are seldom used in practice due to their high construction costs, the multi-dimensional VI histogram can be constructed in just one scan through the data. Through a set of experiments, we show that the multi-dimensional VI histogram is capable of providing more accurate estimations than the techniques currently used in major commercial database management systems, including IBM DB2, Oracle Database, and Microsoft SQL Server.

A multi-dimensional histogram for selectivity estimation and fast approximate query answering