Working with and Visualizing Big Data Efficiently with Python for the DARPA XDATA Program
Research performed under the XDATA program focused on computational techniques and software tools for analyzing large volumes of data, both semi-structured (e.g. tabular, relational, categorical, meta-data) and unstructured (e.g. text, documents, message traffic). Several open source project which h...
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| creator | Oliphant,Travis Wang,Peter Seibert,Stan Rocklin,Matthew Van de Ven,Bryan Sparra,Hunt |
| description | Research performed under the XDATA program focused on computational techniques and software tools for analyzing large volumes of data, both semi-structured (e.g. tabular, relational, categorical, meta-data) and unstructured (e.g. text, documents, message traffic). Several open source project which have seen community and industry adoption grew out of this effort. - Blaze: A collection packages for describing and accessing, and manipulating disparate data sources and types - Numba: A just-in-time function compiler for Python, based on LLVM compiler project allowing researchers to run their Python code near native speeds on CPUs and GPUs. - Dask: Parallelizes generic Python and extends NumPy, Pandas, and Scikit-learn with parallel variants. -Bokeh: Create interactive web applications from Python without having to know Javascript, CSS, or HTML. |
| format | Report |
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| source | DTIC Technical Reports |
| subjects | algorithms Big Data CLUSTERING computer program documentation computer programming Computer Programming and Software DATA VISUALIZATION high performance computing information systems InteractivE machine learning PYTHON PROGRAMMING LANGUAGE SOFTWARE TOOLS web applications workload |
| title | Working with and Visualizing Big Data Efficiently with Python for the DARPA XDATA Program |
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