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

Data heterogeneity hampers the effort to integrate and infer knowledge fromvast heterogeneous data sources. An application case study is described, inwhich the objective was to semantically represent and integrate structured datafrom sensor devices with unstructured data in the form of local indigenousknowledge. However, the semantic representation of these heterogeneous datasources for environmental monitoring systems is not well supported yet. Tocombat the incompatibility issues, a dedicated semantic middleware solution isrequired. In this paper, we describe and evaluate a cross-domain middlewarearchitecture that semantically integrates and generate inference fromheterogeneous data sources. These use of semantic technology for predicting andforecasting complex environmental phenomenon will increase the degree ofaccuracy of environmental monitoring systems.

Semantic Interoperability Middleware Architecture for Heterogeneous Environmental Data Sources