Utilizing Cloud Computing to address big geospatial data challenges

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

“…Building a DC requires addressing the Big Data characteristics of Volume, Velocity, and Variety. To address these issues, Cloud computing appears as a viable solution for processing data and increasing the Value of these data by generating usable and useful products (Yang, Huang, Li, Liu, & Hu, 2017;Yang, Yu, Hu, Yongyao, & Yun, 2017).…”

Current Efforts for Prediction and Assessment of Natural Disasters: Earthquakes, Tsunamis, Volcanic eruptions, Hurricanes, Tornados, and Floods

“…They introduced a three-layer system: geospatial big data integration and management, geospatial big data analytics, and geospatial big data service platform. The use of cloud computing for big geospatial data was presented by Yang et al [227]. Dhamodaran et al [230] discussed big data implementation of natural disaster monitoring and alerting system in real-time social network using Hadoop technology and Twitter-based information.…”

“…Although the term big data is still not well de ned, it is broadly de ned in the literature as massive structured or unstructured data volumes that cannot be stored, processed, and analyzed using conventional hardware and software technologies [225][226][227]. Also, it has been characterized by a simple concept based on a speci c number of Vs. Figure 2 shows the evolution of that concept.…”

“…Based on this de nition, a new dimension is added to the concept of big data, veracity, forming the 4th V (usefulness of results obtained from data analysis) ( Figure 2(b)). Recently, a new measure of complexity has been added: value, as the 5th V (better decision support, research, and operations) (see Figure 2(c)) [227]. All the aforementioned issues involve and generate speci c challenges and opportunities for research and technological development in speci c scienti c areas.…”

Big Data Management for Cloud-Enabled Geological Information Services

“…Geology is a data intensive science and geological data are characterized with multisource heterogeneity, spatiotemporal variation, correlation, uncertainty, fuzziness, and nonlinearity. Therefore, the geological cloud has a certain degree of confidentiality and it is highly domain-specific; meanwhile, it is developed on the basis of a large amount of geological data accumulated over a long period of time [5,11]. There are many real-time data generated from some issues like geological disasters and geological environment.…”