{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101629"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101629","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A scalable direct manipulation engine for position-aware presentational data management","abstract":"With the explosion of data, large datasets become more common for data analysis. How- ever, existing analytic tools are lack of scalability and large-scale data management tools are lack of interactivity. A lot of data analysis tasks are based on the order of data, we are proposing the very first positional storage engine supporting persistence and maintenance of orders for large datasets and allow direct manipulation on orders. We introduce a sparse monotonic order statistic structure for persisting and maintaining order. We also show how to support multiple orders and optimize the storage. After that, we demonstrate a buffered storage manager to ensure the direct manipulation interactivity. Last, we show our final system DataSpread which is interactive and scalable. In the end, we hope that our solution can point out a potential direction to support data analysis for large-scale data.","abstract_html":"With the explosion of data, large datasets become more common for data analysis. How- ever, existing analytic tools are lack of scalability and large-scale data management tools are lack of interactivity. A lot of data analysis tasks are based on the order of data, we are proposing the very first positional storage engine supporting persistence and maintenance of orders for large datasets and allow direct manipulation on orders. We introduce a sparse monotonic order statistic structure for persisting and maintaining order. We also show how to support multiple orders and optimize the storage. After that, we demonstrate a buffered storage manager to ensure the direct manipulation interactivity. Last, we show our final system DataSpread which is interactive and scalable. In the end, we hope that our solution can point out a potential direction to support data analysis for large-scale data.","abstract_has_math":false,"creators":["Zhou, Xinyan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Chang, Kevin Chen-Chuan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:18:04Z","date_published":"2018-09-27T16:18:04Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Order","Direct Manipulation","Positional Indexing"],"languages":["en"],"rights":["Copyright 2018 Xinyan Zhou"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101629","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chang, Kevin Chen-Chuan"]},{"key":"dc:creator","label":"Author","values":["Zhou, Xinyan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:18:04Z","2018-07-20","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Order","Direct Manipulation","Positional Indexing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Xinyan Zhou"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101629"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["With the explosion of data, large datasets become more common for data analysis. How- ever, existing analytic tools are lack of scalability and large-scale data management tools are lack of interactivity. A lot of data analysis tasks are based on the order of data, we are proposing the very first positional storage engine supporting persistence and maintenance of orders for large datasets and allow direct manipulation on orders. We introduce a sparse monotonic order statistic structure for persisting and maintaining order. We also show how to support multiple orders and optimize the storage. After that, we demonstrate a buffered storage manager to ensure the direct manipulation interactivity. Last, we show our final system DataSpread which is interactive and scalable. In the end, we hope that our solution can point out a potential direction to support data analysis for large-scale data.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Xinyan Zhou, accepted the attached license on 2018-07-20 at 11:22.","The student, Xinyan Zhou, submitted this Thesis for approval on 2018-07-20 at 11:50.","This Thesis was approved for publication on 2018-07-20 at 13:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12948 on 2018-09-27 at 10:50:24","Made available in DSpace on 2018-09-27T16:18:04Z (GMT). No. of bitstreams: 2 ZHOU-THESIS-2018.pdf: 848105 bytes, checksum: 8cbb9cea56b2d787fd72c1c13d737de0 (MD5) LICENSE.txt: 4208 bytes, checksum: 824a8799838251e4ad7c3556db1447b2 (MD5) Previous issue date: 2018-07-20"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A scalable direct manipulation engine for position-aware presentational data management"]}]}],"canonical_facts":{"dc:contributor":["Chang, Kevin Chen-Chuan"],"dc:creator":["Zhou, Xinyan"],"dc:date":["2018-09-27T16:18:04Z","2018-07-20","2018-08"],"dc:description":["With the explosion of data, large datasets become more common for data analysis. How- ever, existing analytic tools are lack of scalability and large-scale data management tools are lack of interactivity. A lot of data analysis tasks are based on the order of data, we are proposing the very first positional storage engine supporting persistence and maintenance of orders for large datasets and allow direct manipulation on orders. We introduce a sparse monotonic order statistic structure for persisting and maintaining order. We also show how to support multiple orders and optimize the storage. After that, we demonstrate a buffered storage manager to ensure the direct manipulation interactivity. Last, we show our final system DataSpread which is interactive and scalable. In the end, we hope that our solution can point out a potential direction to support data analysis for large-scale data.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Xinyan Zhou, accepted the attached license on 2018-07-20 at 11:22.","The student, Xinyan Zhou, submitted this Thesis for approval on 2018-07-20 at 11:50.","This Thesis was approved for publication on 2018-07-20 at 13:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12948 on 2018-09-27 at 10:50:24","Made available in DSpace on 2018-09-27T16:18:04Z (GMT). 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