Future of Big Earth Data Analytics

The state of the art of Big Earth Data Analytics can be expected to evolve rapidly in the coming years. The forces driving evolution come from both growth in the data and advancement in the field of data analytics. In the data area, advances in sensor instrumentation and platform miniaturization are increasing both data resolution and coverage, resulting in enormous growth in data Volume. Increases in temporal resolution in particular also generate demands for higher data Velocity. At the same time, the proliferation of instruments and the platforms on which they reside is increasing the Variety of datasets. The Variety increase in turn leads to questions about the Veracity of the data. In the algorithm area, powerful machine learning methods are coming to the fore, particularly Deep Neural Networks. These are powerful at detecting interesting features in the data, integrating many different measurements (i.e., data fusion), and classification problems. However, they are still challenging when seeking explanations of how natural or socio-economic phenomena work using Earth Observations. Thus, classical analysis techniques will remain relevant when the emphasis is on forming or testing explanations, as well as to support interactive data exploration

Next-generation big data analytics: state of the art, challenges, and future research topics

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.This work was supported in part by the “Open3D: Collaborative Editing for 3D Virtual Worlds” [EPSRC (EP/M013685/1)], in part by the “Distributed Java Infrastructure for Real-Time Big-Data” (CAS14/00118), in part by eMadrid (S2013/ICE-2715), in part by the HERMES-SMARTDRIVER (TIN2013-46801-C4-2-R), and in part by the AUDACity (TIN2016-77158-C4-1-R). Paper no. TII-16-1