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

The similarity between trajectory patterns in clustering has played animportant role in discovering movement behaviour of different groups of mobileobjects. Several approaches have been proposed to measure the similaritybetween sequences in trajectory data. Most of these measures are based onEuclidean space or on spatial network and some of them have been concerned withtemporal aspect or ordering types. However, they are not appropriate tocharacteristics of spatiotemporal mobility patterns in wireless networks. Inthis paper, we propose a new similarity measure for mobility patterns incellular space of wireless network. The framework for constructing our measureis composed of two phases as follows. First, we present formal definitions tocapture mathematically two spatial and temporal similarity measures formobility patterns. And then, we define the total similarity measure by means ofa weighted combination of these similarities. The truth of the partial andtotal similarity measures are proved in mathematics. Furthermore, instead ofthe time interval or ordering, our work makes use of the timestamp at which twomobility patterns share the same cell. A case study is also described to give acomparison of the combination measure with other ones.

A weighted combination similarity measure for mobility patterns in wireless networks