Big data methods and techniques applied to geospatial data
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Big Data Geospatial builds upon Big Data Analytics and refers to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set - (DSTL). Big data, machine learning, and predictive data analytics allows researchers to extract insights from both scientific instruments and computational simulations (4th Paradigm) - (ACM Comm). |
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Provenance is information about entities, activities, and people involved in producing a piece of data or thing, which can be used to form assessments about its quality, reliability or trustworthiness - (W3C PROV). |
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Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. Deep learning and Convolutional Neural Networks (CNNs) - a sub type of machine learning - consists of multiple hidden layers in an artificial neural network - (Wikipedia). |
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Simulation modeling is the process of creating and analyzing a digital prototype of a physical model to predict its performance in the real world. Models and simulation can be used for analysis and for training. |
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An extremely large database (XLDB) is a database that stores and processes enormous amounts of data and associated records and entries. As the largest database form factor, XLDB is created and managed by very few organizations around the world, typically scientific research institutes that have massive data sets at their disposal. |