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

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.

Spatio-Temporal Range Search using K-DSTH Indexing Structure

“…These structures are also used as base sturctures of lots of spatio-temporal indexing structures. [6] presents an efficient graph-based spatio-temporal indexing method for task-oriented multi-modal scene data organization. This research work proposed spatio-temporal index which is multi-modal and multi-level hybrid in design.…”

A Multiresolution Vector Data Compression Algorithm Based on Space Division

“…Spatial data acquisition efficiency and accuracy have been drastically improved due to fast development of positioning technologies including global navigation satellite systems (GNSS), Bluetooth, Wi-Fi and others equipped on portable mobile devices [1,2]. Volume of big geospatial data in vector model such as trajectory of moving agents and geotagged social media posts grows exponentially and brings more challenges to efficient spatial data management, network transmission and visualization [3][4][5]. Compression of vector spatial data can relieve the pressure of these application scenarios.…”