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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Time series data is usually stored and processed in the form of discrete trajectories of multidimensional measurement points. In order to compare the measurements of a query trajectory to a set of stored trajectories, one needs to calculate similarity between two trajectories. In this paper an efficient algorithm for calculating the similarity is presented for a set of trajectories containing one increasing measurement dimension, for example time series data. An even more efficient version of the algorithm suitable for situations where all dimensions are increasing, such as many spatio-temporal data sets, is also presented. Furthermore, the similarity measurement technique nearly fulfills the requirements of a metric space, which is a clear improvement to the currently used procedure. The performance of the algorithm is validated first by using data measured from a hot strip mill and then by using synthetically generated trajectories. The new algorithm outperforms the currently used procedure by several orders of magnitude, depending on the context of usage.

Efficient Algorithm for Calculating Similarity between Trajectories Containing an Increasing Dimension