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

When do you trust a model? More specifically, when can a model be used for a specific application? This question often takes years of experience and specialized knowledge to answer correctly. Once this knowledge is acquired it must be applied to each application. This involves instrumentation, data collection and finally interpretation. We propose the use of a trained Support Vector Machine (SVM) to give an automated system the ability to make an educated guess as to model applicability. We demonstrate a proof-of-concept which trains a SVM to correctly determine if a particular queueing model is suitable for a specific queue within a streaming system. The SVM is demonstrated using a micro-benchmark to simulate a wide variety of queueing conditions.

Automated Reliability Classification of Queueing Models for Streaming Computation