Big Data and Challenges Sample Clauses

Big Data and Challenges. Relevant aspects for the definition of Big Data are ‘Volume’, ‘Velocity’ and ‘Variety’ as illustrated in the image below. Image: The expansion of data volume, velocity and variety together create the Big Data paradigm. • Volume: Data volumes are growing in all scientific disciplines as new and more connected instrumentation is developed and deployed. In the ocean sciences this growth in data volumes has been characterised by the change in observations from solely ship-board activities to the inclusion of remotely sensed data and the deployment of autonomous vehicles, including Argo floats and glider fleets. Oceanographic models also create large volume output datasets. • Velocity: One of the most revolutionary aspects of connected devices is the speed at which data can be collected, transmitted, processed and integrated. The development of subsea observatories, connected to shore, has driven the push to true real-time data in oceanographic applications. Similarly, increased bandwidth from satellite communications systems means that there are now options to retrieve data in near-real time from research vessels and autonomous vehicles. This latter improvement will grow as low-powered wide area network transmitters become ruggedized for the marine environment. These emerging near- and real-time technologies improve the ability to detect and react to events in the ocean, for example detection of subsea earthquakes feeding tsunami warning systems. • Variety: There are several Big Data applications where the variety of data is not an issue: for example the Twitter dataset contains only (a very large number) of messages with a length no greater than 140 characters. However oceanographic data is much more complex. For example further analysis of a water bottle sample taken at a CTD station on a research cruise might produce tens of measurements for different parameters. The Volume aspect of Big Data presents challenges in working with datasets which are too large to be processed on traditional desktop machines. In order to effectively process the data, workflows must be provided in which the code is moved from a local host to a remote machine so that it is close to the data. Velocity challenges the traditional mode of operation of the National Oceanographic Data Centres; namely delayed mode, quality controlled data delivery. Therefore, there is a social-barrier to be overcome in terms of publishing data in real-time. Some of the technical challenges to be overcome...
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