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A View from ORNL: Scientific Data Research Opportunities in the Big Data Age View Document

Citation

S. Klasky et al., "A View from ORNL: Scientific Data Research Opportunities in the Big Data Age," 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, 2018, pp. 1357-1368.

doi: 10.1109/ICDCS.2018.00136

keywords: {Big Data;parallel processing;scientific information systems;human-generated logs;large-data artifacts;enterprise space;HPC community;scientific community;ORNL;scientific Data research opportunities;Big Data age;computational science;Adaptable I/O System;Data visualization;Task analysis;Analytical models;Big Data;Data models;Computational modeling;Tomography;High Performance Computing;Publish/Subscribe;High Performance I/O;In Situ Visualization},

Abstract

One of the core issues across computer and computational science today is adapting to, managing, and learning from the influx of "Big Data". In the commercial space, this problem has led to a huge investment in new technologies and capabilities that are well adapted to dealing with the sorts of human-generated logs, videos, texts, and other large-data artifacts that are processed and resulted in an explosion of useful platforms and languages (Hadoop, Spark, Pandas, etc.). However, translating this work from the enterprise space to the computational science and HPC community has proven somewhat difficult, in part because of some of the fundamental differences in type and scale of data and timescales surrounding its generation and use. We describe a forward-looking research and development plan which centers around the concept of making Input/Output (I/O) intelligent for users in the scientific community, whether they are accessing scalable storage or performing in situ workflow tasks. Much of our work is based on our experience with the Adaptable I/O System (ADIOS 1.X), and our next generation version of the software ADIOS 2.X [1].

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