A Geospatial Information Grid Framework for Geological Survey

Abstract

The use of digital information in geological fields is becoming very important. Thus, informatization in geological surveys should not stagnate as a result of the level of data accumulation. The integration and sharing of distributed, multi-source, heterogeneous geological information is an open problem in geological domains. Applications and services use geological spatial data with many features, including being cross-region and cross-domain and requiring real-time updating. As a result of these features, desktop and web-based geographic information systems (GISs) experience difficulties in meeting the demand for geological spatial information. To facilitate the real-time sharing of data and services in distributed environments, a GIS platform that is open, integrative, reconfigurable, reusable and elastic would represent an indispensable tool. The purpose of this paper is to develop a geological cloud-computing platform for integrating and sharing geological information based on a cloud architecture. Thus, the geological cloud-computing platform defines geological ontology semantics; designs a standard geological information framework and a standard resource integration model; builds a peer-to-peer node management mechanism; achieves the description, organization, discovery, computing and integration of the distributed resources; and provides the distributed spatial meta service, the spatial information catalog service, the multi-mode geological data service and the spatial data interoperation service. The geological survey information cloud-computing platform has been implemented, and based on the platform, some geological data services and geological processing services were developed. Furthermore, an iron mine resource forecast and an evaluation service is introduced in this paper.

A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data

“…The GSICCP is vertically divided into five layers: a data layer, a fabric layer, a resources layer, a discovery and integration layer, and an application and representation layer [49]. In this paper, which is based on the architecture of the GSICCP, we intend to store and discover massive geological structured and unstructured data.…”

“…The data layer and fabric layer are maintained in their original conditions, and a geological content discovery portal is built in the application and representation layer. Detailed descriptions of these layers can be found in the literature [49]. The architecture of the GSISSP is shown in Figure 1.…”

“…To improve the access The resource layer is the foundation of the GSISSP, consisting of certain pivotal modules, including a geological survey information cloud (GSI-CLOUD) resource integration middleware, GSI-CLOUD meta-service libraries, GSI-CLOUD workflow service middleware, and GSI-CLOUD service engine middleware. MapGIS 10 (a geographic information system platform for managing and analyzing spatial data) and IGServer (a MapGIS software package for map service publication) are integrated into this layer [49]. In addition, the Hadoop ecosystem is integrated into this layer to provide storage and processing for unstructured data.…”

“…All of the spatial processing work will finally be concentrated on the feature processing, as each spatial feature contains attributes, references and geometries. Here, we utilize the spatial data organization model of MapGIS 10, and the GIS data model ontology inherits the ontology in the GSICCP [49]. 8.…”

“…This paper attempts to explore the methods of applying massive geological data from the perspective of unstructured data, structured data and spatial service integration, which could effectively fuse heterogeneous information and improve the quality of geological content services. In this paper, we establish a knowledge-driven geological survey information smart-service platform (GSISSP) based on our previously developed geological survey information cloud-computing platform (GSICCP) [49]. NoSQL technology is integrated into GSISSP to organize complex unstructured data, and ontologies are imported to build semantic and knowledgeable associations.…”