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.
Highlights
High-performance scene data organization is one of the most common and fundamental functions of visualization in the field of geographic information systems (GIS) in the era of big data and cloud computing for smart cities [1,2]
With the development of social networks, sensor networks, and the Internet of Things (IOT), scene data organization in GIS has expanded into three spaces: cyber, physical, and social [3,4,5]
Most of the existing spatio-temporal indexing methods index time and spatial dimension information and aim at maximizing input/output (I/O) of the data [6,7]. They cannot efficiently organize and manage the associated dynamic multi-modal scene data for diverse scene tasks, or respond to complex spatio-temporal relation queries in real time
Summary
The scene data management system service is implemented by Java 1.8, while the graph model is supported by Neo4j Community Edition v3.1.1 (a native graph database). The goals of the UQE-Index are to address the efficiency of real-time access and management of Internet of Things data with efficient I/O performance and a multi-dimensional query ability simultaneously This method is selected to carry out comparative experiments to illustrate the advantages of the STR-graph. A view-only visualization task is a typical I/O-intensive application, and it is a basic function for which the spatio-temporal index must be satisfied Querying in this application scene consists of searching the objects that meet the conditions within a given time interval and spatial range. A prototype system was implemented to support organization and query of scene data This system has a high-performance throughput capability, and is able to support diversified spatio-temporal relation queries through pattern matching to meet the view-only, analytical, and explorative visualization tasks.
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