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

Machine learning techniques for data stream analysis suffer from concept drifts such as changed user preferences, varying weather conditions, or economic changes. These concept drifts cause wrong predictions and lead to incorrect business decisions. Concept drift detection methods such as adaptive windowing (Adwin) allow for adapting to concept drifts on the fly. In this paper, we examine Adwin in detail and point out its throughput bottlenecks.We then introduce several parallelization alternatives to address these bottlenecks. Our optimizations lead to a speedup of two orders of magnitude over the original Adwin implementation. Thus, we explore parallel adaptive windowing to provide scalable concept detection for high-velocity data streams with millions of tuples per second.

Scalable Detection of Concept Drifts on Data Streams with Parallel Adaptive Windowing