Abstract
With the widespread deployment of ground, air and space sensor sources (internet of things or IoT, social networks, sensor networks), the integrated applications of real-time geospatial data from ubiquitous sensors, especially in public security and smart city domains, are becoming challenging issues. The traditional geographic information system (GIS) mostly manages time-discretized geospatial data by means of the Structured Query Language (SQL) database management system (DBMS) and emphasizes query and retrieval of massive historical geospatial data on disk. This limits its capability for on-the-fly access of real-time geospatial data for online analysis in real time. This paper proposes a hybrid database organization and management approach with SQL relational databases (RDB) and not only SQL (NoSQL) databases (including the main memory database, MMDB, and distributed files system, DFS). This hybrid approach makes full use of the advantages of NoSQL and SQL DBMS for the real-time access of input data and structured on-the-fly analysis results which can meet the requirements of increased spatio-temporal big data linking analysis. The MMDB facilitates real-time access of the latest input data such as the sensor web and IoT, and supports the real-time query for online geospatial analysis. The RDB stores change information such as multi-modal features and abnormal events extracted from real-time input data. The DFS on disk manages the massive geospatial data, and the extensible storage architecture and distributed scheduling of a NoSQL database satisfy the performance requirements of incremental storage and multi-user concurrent access. A case study of geographic video (GeoVideo) surveillance of public security is presented to prove the feasibility of this hybrid organization and management approach.
Highlights
With the widespread deployment of ground, air and space sensor sources, the integrated applications of real-time geospatial data from ubiquitous sensors have already become challenging issues, especially in the case of public security management and the facility management of smart city and present characteristics of 4V categories [1,2]
The traditional geographic information system (GIS) aims to map the “snapshot” of the geographical world in a moment in an structured format into commercial relational databases (RDBs) such as Oracle and MySQL, using geospatial data persistence followed by further development and integration of on-demand application functions operated on “outdated” database records [5]
Kim et al utilized Redis to solve the high traffics of web services in concurrent access [17]. These not only SQL (NoSQL) database management system (DBMS) offer the benefits of high-performance writing/querying for large volume real-time input data, a novel approach for organization of real-time geospatial data is needed to cope with both fast-growing real-time input data and on-the-fly analysis results for real-time geo-processing
Summary
With the widespread deployment of ground, air and space sensor sources (internet of things or IoT, social networks and sensor networks), the integrated applications of real-time geospatial data from ubiquitous sensors have already become challenging issues, especially in the case of public security management and the facility management of smart city and present characteristics of 4V categories (volume, velocity, variety and value) [1,2]. Kim et al utilized Redis to solve the high traffics of web services in concurrent access [17] These NoSQL DBMS offer the benefits of high-performance writing/querying for large volume real-time input data, a novel approach for organization of real-time geospatial data is needed to cope with both fast-growing real-time input data and on-the-fly analysis results for real-time geo-processing. To lay the foundations for online GIS analysis in real time, this manuscript presents a hybrid database organization and management approach of SQL database RDBs and NoSQL databases (including the main memory database, MMDB, and distributed files systems, DFS). The RDB stores change information like multi-modal features and abnormal events extracted from real-time input data; the DFS on disk manages the massive geospatial data, and the extensible storage architecture and distributed scheduling of a NoSQL database satisfy the performance requirements of incremental storage and multi-user concurrent access. NoSQL–SQL Hybrid Organization and Management Approach for Real-Time Geospatial Data
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