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

This study proposes an efficient exact k-flexible aggregate nearest neighbor(k-FANN) search algorithm in road networks using the M-tree. Thestate-of-the-art IER-kNN algorithm used the R-tree and pruned off unnecessarynodes based on the Euclidean coordinates of objects in road networks. However,IER-kNN made many unnecessary accesses to index nodes since the Euclideandistances between objects are significantly different from the actualshortest-path distances between them. In contrast, our algorithm proposed inthis study can greatly reduce unnecessary accesses to index nodes compared withIER-kNN since the M-tree is constructed based on the actual shortest-pathdistances between objects. To the best of our knowledge, our algorithm is thefirst exact FANN algorithm that uses the M-tree. We prove that our algorithmdoes not cause any false drop. In conducting a series of experiments usingvarious real road network datasets, our algorithm consistently outperformedIER-kNN by up to 6.92 times.

Efficient Exact k-Flexible Aggregate Nearest Neighbor Search in Road Networks Using the M-tree