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

Distributed computing which uses Web services as fundamental elements,enables high-speed development of software applications through composing manyinteroperating, distributed, re-usable, and autonomous services. As afundamental challenge for service developers, service composition must fulfilfunctional requirements and optimise Quality of Service (QoS) attributes,simultaneously. On the other hand, huge amounts of data have been created byadvances in technologies, which may be exchanged between services.Data-intensive Web services are of great interest to implement data-intensiveprocesses. However, current approaches to Web service composition have omittedeither the effect of data, or the distribution of services. EvolutionaryComputing (EC) techniques allow for the creation of compositions that meet allthe above factors. In this paper, we will develop Genetic Algorithm (GA)-basedapproach for solving the problem of distributed data-intensive Web servicecomposition (DWSC). In particular, we will introduce two new heuristics, i.e.Longest Common Subsequence(LCS) distance of services, in designing crossoveroperators. Additionally, a new local search technique incorporating distance ofservices will be proposed.

Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition