Massive Data Sets: Proceedings of a Workshop

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It was also agreed that there is a need to change or improve data analysis presentation tools. In this connection, better design of hierarchical visual display and improved techniques for conveying or displaying variability and bias in models were suggested. It was also agreed that a more broad-based education will be required for statisticians, one that includes better links between statistics and computer science. Research community and paradigm issues include a need to identify success stories regarding the use and analysis of massive data sets; increase the visibility of concerns about massive data sets in professional and educational settings; and explore relevant literature in computer science, statistical mechanics, and other areas.

Discussions during the workshop pointed to the need for a wider variety of statistical models, beyond the traditional linear ones that work well when data is essentially "clean" and possesses nice properties. A dilemma is that analysis of massive, complex data generates involved and complicated answers, yet there is a perceived need to keep things simple. The culminating activity of the workshop was a panel discussion on fundamental issues and grand challenges during which participants exchanged views on basic concerns and research issues raised to varying extents in the workshop's three group discussion sessions.

To facilitate the discussion the panel moderator posed four questions selected from among those generated by panel members prior to the session. The proceedings reflect attempts by workshop participants to address these and related questions. There were significant differences of opinion, but some agreement on items for ongoing exploration and attention—summarized above and listed in Part IV —was reached.

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Opening Remarks Jon Kettenring, Bellcore. Dumais, Bellcore.

Russell, Advanced Micro Devices. Kindermann and Marshall M. DeBerry, Jr. Department of Justice. McIntosh, Bellcore. Sampson, University of Washington. Amant and Paul R. Cohen, University of Massachusetts. Visualizing Large Datasets Stephen G. Solka, W. Poston, and D. Wegman, George Mason University. Items for Ongoing Consideration. Closing Remarks Jon Kettenring, Bellcore. Appendix: Workshop Participants. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

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Printed in the United States of America. Page iii Share Cite. Committee on Applied and Theoretical Statistics. Jon R. Kettering, Bellcore, Chair. Richard A. Berk, University of California, Los Angeles. Lawrence D. Brown, University of Pennsylvania. Advertisement Hide. International Supercomputing Conference. Conference paper. This is a preview of subscription content, log in to check access. Ahrens, J. Chen, J. In: Computational Science and Discovery, vol. Chiueh, T. Foley, J.

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List of datasets for machine-learning research

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Managing Large Datasets with Python and HDF5 - O'Reilly Webcast

IDIAP, Retrieved 26 March Scientific Reports. Bibcode : NatSR I have every publicly available Reddit comment for research. Any interest in this? Kowsari, D. Brown, M. Heidarysafa, K. Jafari Meimandi, M. Gerber and L. Barnes, "Web of Science Dataset", doi: Association for Computational Linguistics, Journal of Big Data.

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