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

A large volume of content generated by online users is geo-tagged and thisprovides a rich source for querying in various location-based services. Animportant class of queries within such services involves the associationbetween content and locations. In this paper, we study two types of queries onstreaming geo-tagged data: 1) "Top-k reverse frequent spatial queries", wheregiven a term, the goal is to find top K locations where the term is frequent,and 2) "Term frequency spatial queries", which is finding the expectedfrequency of a term in a given location. To efficiently support these queriesin a streaming setting, we model terms as events and explore a probabilisticmodel of geographical distribution that allows us to estimate the frequency ofterms in locations that are not kept in a stream sketch or summary. We studythe back-and-forth relationship between the efficiency of queries, theefficiency of updates and the accuracy of the results and identify some sweetspots where both efficient and effective algorithms can be developed. Wedemonstrate that our method can be extended to support multi-term queries. Toevaluate the efficiency of our algorithms, we conduct experiments on arelatively large collection of both geo-tagged tweets and geo-tagged Flickrphotos. The evaluation reveals that our proposed method achieves a highaccuracy when only a limited amount of memory is given. Also the query time isimproved, compared to a recent baseline, by 2-3 orders of magnitude withoutmuch loss in accuracy and that the update time can further be improved by atleast an order of magnitude under some term distributions or update strategies.

Tracking Where Events Take Place: Reverse Spatial Term Queries on Streaming Data