A NoSQL–SQL Hybrid Organization and Management Approach for Real-Time Geospatial Data: A Case Study of Public Security Video Surveillance

Wu, Chen; Zhu, Qing; Zhang, Yeting; Du, Zhiqiang; Ye, Xinyue; Qin, Han; Zhou, Yan

doi:10.3390/ijgi6010021

Cited by 29 publications

(17 citation statements)

References 21 publications

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“…Dealing with a similar problem, Choi and Lee [1] proposed an approach to evaluate the surveillance coverage index to quantitatively measure how effectively a video surveillance system can monitor a targeted area. The problem of organizing and managing real-time geospatial data for public security video surveillance was addressed by Wu et al [17]. They proposed a hybrid NoSQL-SQL approach for real-time data access and structured on-the-fly analysis which can meet the requirements of increased spatio-temporal big data linking analysis.…”

Section: Related Workmentioning

confidence: 99%

A Method for Estimating Surveillance Video Georeferences

Milosavljević¹,

Rančić²,

Dimitrijevic³

et al. 2017

IJGI

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Abstract:The integration of a surveillance camera video with a three-dimensional (3D) geographic information system (GIS) requires the georeferencing of that video. Since a video consists of separate frames, each frame must be georeferenced. To georeference a video frame, we rely on the information about the camera view at the moment that the frame was captured. A camera view in 3D space is completely determined by the camera position, orientation, and field-of-view. Since the accurate measuring of these parameters can be extremely difficult, in this paper we propose a method for their estimation based on matching video frame coordinates of certain point features with their 3D geographic locations. To obtain these coordinates, we rely on high-resolution orthophotos and digital elevation models (DEM) of the area of interest. Once an adequate number of points are matched, Levenberg-Marquardt iterative optimization is applied to find the most suitable video frame georeference, i.e., position and orientation of the camera.

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Section: Related Workmentioning

confidence: 99%

A Method for Estimating Surveillance Video Georeferences

Milosavljević¹,

Rančić²,

Dimitrijevic³

et al. 2017

IJGI

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“…The massive use of geo-referenced data sets in many fields of science and economy including earth observation [1], environmental sciences [2], city planning [3], BIM [3,4], real-time processing [5,6], and analytics for geospatial data [5] makes geospatial data management increasingly a central task in the workflow of geospatial data processing [1,5,7]. Recent approaches consider open data platforms and containers to handle big geospatial raster and vector data efficiently.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Data Management Research: Progress and Future Directions

Breunig

Bradley

Jahn

et al. 2020

IJGI

102

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Without geospatial data management, today’s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis.

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“…The second consists of hybrid indexing methods that synthesize the advantages of different indexes to compensate for the existing methods [17]. The hybrid index methods are gradually trending and have become commonly used in distributed file systems and the NoSQL (Not Only Structured Query Language) database, which maximizes I/O via horizontal extension [18]. Hybrid indexing methods can be divided into two categories: general and professional.…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Zhu

Liu

et al. 2018

IJGI

Self Cite

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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.

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A NoSQL–SQL Hybrid Organization and Management Approach for Real-Time Geospatial Data: A Case Study of Public Security Video Surveillance

Cited by 29 publications

References 21 publications

A Method for Estimating Surveillance Video Georeferences

A Method for Estimating Surveillance Video Georeferences

Geospatial Data Management Research: Progress and Future Directions

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

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