Next Article in Journal
Digital Image Correlation (DIC) Analysis of the 3 December 2013 Montescaglioso Landslide (Basilicata, Southern Italy): Results from a Multi-Dataset Investigation
Previous Article in Journal
Raising Semantics-Awareness in Geospatial Metadata Management
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(9), 371; https://doi.org/10.3390/ijgi7090371

An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization

1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
2
School of Resource and Environment, University of Electric Science and Technology, Chengdu 611731, China
3
Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China
*
Authors to whom correspondence should be addressed.
Received: 13 July 2018 / Revised: 2 September 2018 / Accepted: 4 September 2018 / Published: 8 September 2018
Full-Text   |   PDF [6520 KB, uploaded 8 September 2018]   |  

Abstract

Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries. View Full-Text
Keywords: multi-modal scene data; graph-based index; spatio-temporal relation query; visualization task; data organization multi-modal scene data; graph-based index; spatio-temporal relation query; visualization task; data organization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Feng, B.; Zhu, Q.; Liu, M.; Li, Y.; Zhang, J.; Fu, X.; Zhou, Y.; Li, M.; He, H.; Yang, W. An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization. ISPRS Int. J. Geo-Inf. 2018, 7, 371.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top