Near-Real-Time OGC Catalogue Service for Geoscience Big Data

Geoscience data are typically big data, and they are distributed in various agencies and individuals worldwide. Efficient data sharing and interoperability are important for managing and applying geoscience data. The OGC (Open Geospatial Consortium) Catalogue Service for the Web (CSW) is an open interoperability standard for supporting the discovery of geospatial data. In the past, regular OGC catalogue services have been studied, but few studies have discussed a near-real-time OGC catalogue service for geoscience big data. A near-real-time OGC catalogue service requires frequent updates of a metadata repository in a short time. When dealing with massive amounts of geoscience data, this comprises an extremely challenging issue. Discovering these data via an OGC catalogue service in near real-time is desirable. In this study, we focus on how the near-real-time OGC catalogue service is realized through several lightweight data structures, algorithms, and tools. We propose a framework of a near-real-time OGC catalogue service and discuss each element of the framework to which more attention should be paid when dealing with the massive amounts of real-time data, followed by a review of several methods that need to be considered in a near-real-time OGC CSW service. A case study on providing an OGC catalogue service to Unidata real-time data is presented to demonstrate how specific methods are utilized to deal with real-time data. The goal of this paper is to fill the gap in knowledge regarding an OGC catalogue service for geoscience big data, and it has realistic significance in facilitating a near-real-time OGC catalogue service

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms