{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/85230"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/85230","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Diverse sampling of streaming data","abstract":"This thesis addresses the problem of diverse sampling as a dispersion problem and proposes solutions that are optimized for large streaming data. Finding the optimal solution to the dispersion problem is NP-hard. Therefore, existing and proposed solutions are approximation algorithms. This work evaluates the performance of dierent algorithms in practice and compares them to the theoretical guarantees.","abstract_html":"This thesis addresses the problem of diverse sampling as a dispersion problem and proposes solutions that are optimized for large streaming data. Finding the optimal solution to the dispersion problem is NP-hard. 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