{"id":{"repo_id":"purdue-thes","oai_identifier":"oai:docs.lib.purdue.edu:open_access_dissertations-2595"},"canonical_url":"https://search.dev.ndltd.org/etd/purdue-thes/oai:docs.lib.purdue.edu:open_access_dissertations-2595","repository":{"repo_id":"purdue-thes","name":"Purdue University","base_url":"https://docs.lib.purdue.edu/do/oai/"},"display":{"title":"Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks","abstract":"The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks.","abstract_html":"The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. 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Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks."]},{"key":"dc:title","label":"Title","values":["Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks"]}]}],"canonical_facts":{"dc:contributor":["Ananth Y Grama","Dongyan Xu","Sonia Fahmy","Mathias Payer","Aniket Kate"],"dc:creator":["Kambatla, Karthik Shashank"],"dc:description.abstract":["The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks."],"dc:identifier":["https://docs.lib.purdue.edu/open_access_dissertations/1379"],"dc:subject":["big data","distributed computing","distributed systems"],"dc:title":["Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:54:38Z"}