A spatiotemporal RDF stream model for effective representation of temporal and spatial data

Xin Wang; Chaojie Zhang

doi:10.1117/12.3026296

27 March 2024 A spatiotemporal RDF stream model for effective representation of temporal and spatial data

Xin Wang, Chaojie Zhang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131054P (2024) https://doi.org/10.1117/12.3026296
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Resource Description Framework (RDF), a metadata model, has become a commonly utilized tool in the extraction of semantic information, the unified organization, and the intelligent processing of vast amounts of data, due to its machine-readable nature. However, the conventional RDF model is inadequate in accurately representing time series data that incorporate spatial information, thus designing an RDF stream model that aligns with temporal and spatial data is a new challenge. Our paper is devoted to designing and constructing a spatiotemporal stream RDF model suitable for spatiotemporal data. The structure devised herein reduces redundancy in temporal and spatial information compared to traditional RDF models, better representing the dynamism of spacetime. Furthermore, the reliability and effectiveness of our model structure have been empirically substantiated.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Wang and Chaojie Zhang "A spatiotemporal RDF stream model for effective representation of temporal and spatial data", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131054P (27 March 2024); https://doi.org/10.1117/12.3026296

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available