In this thesis, the recent trends in cloud computing, via virtual machine hosted servers, are applied to the field of big data visualization. In particular, we investigate the visualization of terrain data acquired from several major open data sets with a graphics library for browser based rendering. Similar terrain data visualization solutions have not fully taken advantage of remote computational resources. In this thesis, we show that, by using a collection of Amazon EC-2 machines for fetching and decoding of terrain data, in conjunction with modern graphics libraries, three dimensional terrain information may be viewed and interacted with by many remote clients within the browser. Data pre-fetching and a parallel producer-consumer model implementation of each server further improve performance. This study should enable and expedite existing and future study in the terrain data visualization field.
","abstract_html":"<p>In this thesis, the recent trends in cloud computing, via virtual machine hosted servers, are applied to the field of big data visualization. In particular, we investigate the visualization of terrain data acquired from several major open data sets with a graphics library for browser based rendering. Similar terrain data visualization solutions have not fully taken advantage of remote computational resources. In this thesis, we show that, by using a collection of Amazon EC-2 machines for fetching and decoding of terrain data, in conjunction with modern graphics libraries, three dimensional terrain information may be viewed and interacted with by many remote clients within the browser. Data pre-fetching and a parallel producer-consumer model implementation of each server further improve performance. This study should enable and expedite existing and future study in the terrain data visualization field.</p>","abstract_has_math":false,"creators":["Larrick, Gregory J."],"institution":null,"degree_name":"Master of Science (MS) in Computer Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-01-01T08:00:00Z","date_published":"2019-01-01T08:00:00Z","updated_at":"2026-07-24T02:13:16Z","subjects":["Databases and Information Systems","Other Computer Sciences","Systems Architecture"],"languages":[],"rights":["Access is available to all users"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dc.ewu.edu/theses/565","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Larrick, Gregory J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS) in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Databases and Information Systems","Other Computer Sciences","Systems Architecture"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Access is available to all users"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dc.ewu.edu/theses/565"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, the recent trends in cloud computing, via virtual machine hosted servers, are applied to the field of big data visualization. In particular, we investigate the visualization of terrain data acquired from several major open data sets with a graphics library for browser based rendering. Similar terrain data visualization solutions have not fully taken advantage of remote computational resources. In this thesis, we show that, by using a collection of Amazon EC-2 machines for fetching and decoding of terrain data, in conjunction with modern graphics libraries, three dimensional terrain information may be viewed and interacted with by many remote clients within the browser. Data pre-fetching and a parallel producer-consumer model implementation of each server further improve performance. This study should enable and expedite existing and future study in the terrain data visualization field.
"]},{"key":"dc:title","label":"Title","values":["The application of cloud resources to terrain data visualization"]}]}],"canonical_facts":{"dc:creator":["Larrick, Gregory J."],"dc:description.abstract":["In this thesis, the recent trends in cloud computing, via virtual machine hosted servers, are applied to the field of big data visualization. In particular, we investigate the visualization of terrain data acquired from several major open data sets with a graphics library for browser based rendering. Similar terrain data visualization solutions have not fully taken advantage of remote computational resources. In this thesis, we show that, by using a collection of Amazon EC-2 machines for fetching and decoding of terrain data, in conjunction with modern graphics libraries, three dimensional terrain information may be viewed and interacted with by many remote clients within the browser. Data pre-fetching and a parallel producer-consumer model implementation of each server further improve performance. This study should enable and expedite existing and future study in the terrain data visualization field.
"],"dc:identifier":["https://dc.ewu.edu/theses/565"],"dc:rights":["Access is available to all users"],"dc:subject":["Databases and Information Systems","Other Computer Sciences","Systems Architecture"],"dc:title":["The application of cloud resources to terrain data visualization"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS) in Computer Science"]},"updated_at":"2026-07-24T02:13:16Z"}