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

Provenance is the derivation history of information about the origin of dataand processes. For a highly dynamic system such as the cloud, provenance mustbe effectively detected to be used as proves to ensure accountability duringdigital forensic investigations. This paper proposes active-threaded provenancecognition algorithms that ensure effective and high speed detection ofprovenance information in the activity layer of the cloud. The algorithms alsosupport encapsulation of the provenance information on specific targets.Performance evaluation of the proposed algorithms reveal mean delay of 8.198seconds that is below the pre-defined benchmark of 10 seconds. Standarddeviation and cumulative frequencies for delays are found to be 1.434 and 45.1%respectively.

Active-Threaded Algorithms for Provenance Cognition in the Cloud preserving Low Overhead and Fault Tolerance