{"id":15,"date":"2017-06-09T13:40:52","date_gmt":"2017-06-09T13:40:52","guid":{"rendered":"http:\/\/vargas-solar.com\/datacentric-sciences\/?page_id=15"},"modified":"2018-03-06T16:28:14","modified_gmt":"2018-03-06T16:28:14","slug":"to-read","status":"publish","type":"page","link":"http:\/\/vargas-solar.com\/datacentric-sciences\/to-read\/","title":{"rendered":"TO READ"},"content":{"rendered":"

Introduction<\/h4>\n
    \n
  1. G. Vargas-solar, \u201cEfficient data management for putting forward data centric sciences,\u201d in Proc. 1st Int Workshop on Data Science: Methodologies and Use-Cases (DaS\u201917) (in press), 2017.<\/li>\n
  2. M. L. Kersten, S. Idreos, S. Manegold, and E. Liarou, \u201cThe Researcher\u2019s Guide to the Data Deluge: Querying a Scientific Database in Just a Few Seconds,\u201d Proc. of the VLDB Endowment (PVLDB), vol. 4, no. 12, 2011.<\/li>\n<\/ol>\n

    Big data analytics environments<\/h4>\n
      \n
    1. M. Athanassoulis and S. Idreos, \u201cDesign Tradeoffs of Data Access Methods,\u201d Proceedings of the ACM SIGMOD International Conference on Management of Data, Tutorial, pp. 2195\u20132200, 2016.<\/li>\n
    2. S. Idreos et al., \u201cPast and Future Steps for Adaptive Storage Data Systems\u202f: From Shallow to Deep Adaptivity.\u201d<\/li>\n
    3. M. Anderson et al., \u201cBridging the Gap Between HPC and Big Data Frameworks,\u201d Vldb, vol. 10, no. 8, pp. 901–912, 2017.<\/li>\n<\/ol>\n

      Exploring and querying data<\/h4>\n
        \n
      1. Wasay, M. Athanassoulis, and S. Idreos, \u201cQueriosity: Automated Data Exploration [Vision],\u201d Proceedings of the IEEE International Congress on Big Data, 2015.<\/li>\n
      2. M. Then, G. Stephan, T. Neumann, and A. Kemper, \u201cAutomatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs,\u201d vol. 10, no. 8, pp. 877\u2013888, 2017.<\/li>\n
      3. M. Athanassoulis, \u201cQuerying Persistent Graphs using Solid State Storage Why Path Processing over Linked Data\u202f?,\u201d no. March, 2013.<\/li>\n
      4. I. Alagiannis, M. Athanassoulis, and A. Ailamaki, \u201cScaling up analytical queries with column-stores,\u201d Proceedings of the Sixth International Workshop on Testing Database Systems – DBTest \u201913, p. 1, 2013.<\/li>\n
      5. S. L. Xi, O. Babarinsa, M. Athanassoulis, and S. Idreos, \u201cBeyond the Wall: Near-Data Processing for Databases,\u201d Proceedings of the 11th International Workshop on Data Management on New Hardware, p. 2:1–2:10, 2015.11.<\/li>\n
      6. M. Joglekar, T. Rekatsinas, H. Garcia-Molina, A. Parameswaran, and C. R\u00e9, \u201cSLiMFast: Guaranteed Results for Data Fusion and Source Reliability,\u201d 2015.<\/li>\n
      7. C. De Sa, K. Olukotun, and C. R\u00e9, \u201cEnsuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling,\u201d pp. 1\u201333, 2016.<\/li>\n<\/ol>\n

        Big data analytics: playing with data<\/h4>\n
          \n
        1. J. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng, \u201cA survey of machine learning for big data processing,\u201d EURASIP Journal on Advances in Signal Processing, vol. 2016, no. 1, p. 67, 2016.S. Hadjis, C. Zhang, I. Mitliagkas, D. Iter, and C. R\u00e9, \u201cOmnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs,\u201d 2016.<\/li>\n
        2. R\u00e9 et al., \u201cMachine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?,\u201d Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 283\u2013284, 2015.<\/li>\n
        3. Wasay, \u201cData Canopy: Accelerating Exploratory Statistical Analysis,\u201d pp. 557\u2013572, 2017.<\/li>\n
        4. \u00a0J. Ratner, S. H. Bach, H. R. Ehrenberg, and C. R\u00e9, \u201cSnorkel: Fast Training Set Generation for Information Extraction.\u201d<\/li>\n<\/ol>\n

           <\/p>\n

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          Introduction G. Vargas-solar, “Efficient data management for putting forward data centric sciences,” in Proc. 1st Int Workshop on Data Science: Methodologies and Use-Cases (DaS’17) (in press), 2017. M. L. Kersten, S. Idreos, S. Manegold, and E. Liarou, “The Researcher’s Guide to the Data Deluge: Querying a Scientific Database in Just a Few Seconds,” Proc. of […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":5,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-15","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/vargas-solar.com\/datacentric-sciences\/wp-json\/wp\/v2\/pages\/15","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/vargas-solar.com\/datacentric-sciences\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/vargas-solar.com\/datacentric-sciences\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/datacentric-sciences\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/datacentric-sciences\/wp-json\/wp\/v2\/comments?post=15"}],"version-history":[{"count":5,"href":"http:\/\/vargas-solar.com\/datacentric-sciences\/wp-json\/wp\/v2\/pages\/15\/revisions"}],"predecessor-version":[{"id":65,"href":"http:\/\/vargas-solar.com\/datacentric-sciences\/wp-json\/wp\/v2\/pages\/15\/revisions\/65"}],"wp:attachment":[{"href":"http:\/\/vargas-solar.com\/datacentric-sciences\/wp-json\/wp\/v2\/media?parent=15"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}