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 paper introduces a scalable approach for probabilistic top-k similarityranking on uncertain vector data. Each uncertain object is represented by a setof vector instances that are assumed to be mutually-exclusive. The objective isto rank the uncertain data according to their distance to a reference object.We propose a framework that incrementally computes for each object instance andranking position, the probability of the object falling at that rankingposition. The resulting rank probability distribution can serve as input forseveral state-of-the-art probabilistic ranking models. Existing approachescompute this probability distribution by applying a dynamic programmingapproach of quadratic complexity. In this paper we theoretically as well asexperimentally show that our framework reduces this to a linear-time complexitywhile having the same memory requirements, facilitated by incremental accessingof the uncertain vector instances in increasing order of their distance to thereference object. Furthermore, we show how the output of our method can be usedto apply probabilistic top-k ranking for the objects, according to differentstate-of-the-art definitions. We conduct an experimental evaluation onsynthetic and real data, which demonstrates the efficiency of our approach.

Scalable Probabilistic Similarity Ranking in Uncertain Databases (Technical Report)