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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Astronomy is well recognized as big data driven science. As the novelobservation infrastructures are developed, the sky survey cycles have beenshortened from a few days to a few seconds, causing data processing pressure toshift from offline to online. However, existing scientific databases focus onoffline analysis of long-term historical data, not real-time and low latencyanalysis of large-scale newly arriving data.  In this paper, a cloud based method is proposed to efficiently analyzescientific events on large-scale newly arriving data. The solution isimplemented as a highly efficient system, namely Aserv. A set of compact datastore and index structures are proposed to describe the proposed scientificevents and a typical analysis pattern is formulized as a set of queryoperations. Domain aware filter, accuracy aware data partition, highlyefficient index and frequently used statistical data designs are four keymethods to optimize the performance of Aserv. Experimental results under thetypical cloud environment show that the presented optimization mechanism canmeet the low latency demand for both large data insertion and scientific eventanalysis. Aserv can insert 3.5 million rows of data within 3 seconds andperform the heaviest query on 6.7 billion rows of data also within 3 seconds.Furthermore, a performance model is given to help Aserv choose the right cloudresource setup to meet the guaranteed real-time performance requirement.

Cloud based Real-Time and Low Latency Scientific Event Analysis