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/

Zendy Plus

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

Zendy Tools

Zendy Open

We study the problem of finding and monitoring fixed-size subgraphs in acontinually changing large-scale graph. We present the first approach that (i)performs worst-case optimal computation and communication, (ii) maintains atotal memory footprint linear in the number of input edges, and (iii) scalesdown per-worker computation, communication, and memory requirements linearly asthe number of workers increases, even on adversarially skewed inputs.  Our approach is based on worst-case optimal join algorithms, recast as adata-parallel dataflow computation. We describe the general algorithm andmodifications that make it robust to skewed data, prove theoretical bounds onits resource requirements in the massively parallel computing model, andimplement and evaluate it on graphs containing as many as 64 billion edges. Theunderlying algorithm and ideas generalize from finding and monitoring subgraphsto the more general problem of computing and maintaining relational equi-joinsover dynamic relations.

Distributed Evaluation of Subgraph Queries Using Worstcase Optimal LowMemory Dataflows