Join processing in unbounded spatial data streams | Zendy

Wendy Osborn | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Join processing in unbounded spatial data streams

Author(s) -

Wendy Osborn

Publication year - 2020

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2020.07.035

Subject(s) - computer science , joins , data stream mining , join (topology) , spatial analysis , spatial database , spatial query , streams , data mining , bounded function , space (punctuation) , distributed computing , information retrieval , computer network , remote sensing , mathematical analysis , mathematics , operating system , search engine , combinatorics , sargable , web search query , programming language , geology

The application of spatial joins for processing queries in spatial data streams is explored in this paper. Although their application has been studied in centralized and distributed systems, it has received little attention in spatial data streams. This work explores a particular issue with spatial join processing in spatial data streams - the lack of a bounded region of space from which the spatial objects are generated. Therefore, two strategies for join processing in spatial data streams in this situation are proposed. Both provide estimation of the common region shared by multiple spatial data streams to best process a spatial join between them. The strategies are evaluated and compared with a recently proposed approach. Results show that one strategy does an excellent job of estimating the common region given no existing bounded regions of space. Other results and future improvements are identified.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research