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

Encouraged by the success of social networking platforms, more and more enterprises are exploring the use of crowd-sourcing as a method for intraorganization knowledge management. There is not much information about their effectiveness though. While there has been some emphasis on studying friend networks, not much emphasis has been given towards understanding other kinds of user behavior like regularity of access or activity. In this paper we present a wavelet-based clustering method to cluster social-network users into different groups based on their temporal behavior and activity profiles. Cluster characterization reveals the underlying user-group characteristics. User data from web and enterprise social-network platforms have been analyzed.

Wavelet-Based Clustering of Social-Network Users Using Temporal and Activity Profiles