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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

As data sources accumulate information and data size escalates it becomes more and more difficult to maintain the correctness and validity of these datasets. Therefore, tools must emerge to facilitate this daunting task. Fact checking usually involves a large number of data sources that talk about the same thing but we are not sure which holds the correct information or which has any information at all about the query we care for. A join among all or some data sources can guide us through a fact-checking process. However, when we want to perform this join on a distributed computational environment such as MapReduce, it is not obvious how to distribute efficiently the records in the data sources to the reduce tasks in order to join any subset of them in a single MapReduce job. To this end, we propose an efficient approach using the multiway join to cross-check these data sources in a single round

Cross-Checking Multiple Data Sources Using Multiway Join in MapReduce