(~200 PB) over the archive volume. disciplines are represented in are being investigated. filtering next 7 years. In a EOSDIS. Python, Fortran, C (4) analyzing time series. given year, EOSDIS languages. Visualization distributes a volume through tools such as that is comparable to Giovanni. the overall cumulative archive volume. The data represent Data was collected None. This use case basically NEX science platform – There are number of the operational time over a period of 27 deals with a single dataset. data management, analytics processes Web-Enabled period of 1984 to years and is being workflow processing, throughout the Landsat Data 2011 for the Landsat processed over a provenance capture; processing pipeline. The (WELD) 4, 5, and 7 satellites period of 5 years. WELD science key analytics is Processing and corresponds to Based on processing algorithms identifying best available 30PB of processed programmatic goals from South Dakota State pixels for data through the of processing University (SDSU), composition pipeline (1PB inputs, several iterations of browse visualization, and spatial aggregation 10PB intermediate, the final product and time-series code; processes as a part of 6PB outputs) over the span of the Global Imagery Browse the overall QA. The project, 150TB/day Service (GIBS) data analytics algorithms are is processed per visualization platform; custom developed for day during USGS data distribution this use case. processing time platform. Custom-built periods. application and libraries built on top of libraries.
(~200 PB) over the next 7 years. In a given year, EOSDIS distributes a volume that is comparable to the overall cumulative archive volume. archive volume. disciplines are represented in EOSDIS. are being investigated. Python, Fortran, C languages. Visualization through tools such as Giovanni. filtering (4) analyzing time series. Web-Enabled Landsat Data (WELD) Processing The data represent the operational time period of 1984 to 2011 for the Landsat 4, 5, and 7 satellites and corresponds to 30PB of processed data through the pipeline (1PB inputs, 10PB intermediate, 6PB outputs) Data was collected over a period of 27 years and is being processed over a period of 5 years. Based on programmatic goals of processing several iterations of the final product over the span of the project, 150TB/day is processed per day during processing time periods. None. This use case basically deals with a single dataset. NEX science platform – data management, workflow processing, provenance capture; WELD science processing algorithms from South Dakota State University (SDSU), browse visualization, and time-series code; Global Imagery Browse Service (GIBS) data visualization platform; USGS data distribution platform. Custom-built application and libraries built on top of libraries. There are number of analytics processes throughout the processing pipeline. The key analytics is identifying best available pixels for composition and spatial aggregation processes as a part of the overall QA. The analytics algorithms are custom developed for this use case.