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

Join-aggregate is an important and widely used operation in database system. However, it is time-consuming to process join-aggregate query in big data environment, especially on MapReduce framework. The main bottlenecks contain two aspects: lots of I/O caused by temporary data and heavy communication overhead between different data nodes during query processing. To overcome such disadvantages, we design a data structure called Reference Primary Key table (RPK-table) which stores the relationship of primary key and foreign key between tables. Based on this structure, we propose an improved algorithm on MapReduce framework for join-aggregate query. Experiments on TPC-H dataset demonstrate that our algorithm outperforms existing methods in terms of communication cost and query response time

RPK-table based efficient algorithm for join-aggregate query on MapReduce