{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102443"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102443","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A distributed workload-aware approach to partitioning geospatial big data for cybergis analytics","abstract":"Numerous applications and scientific domains have contributed to tremendous growth of geospatial data during the past several decades. To resolve the volume and velocity of such big data, distributed system approaches have been extensively studied to partition data for scalable analytics and associated applications. However, previous work on partitioning large geospatial data focuses on bulk-ingestion and static partitioning, hence is unable to handle dynamic variability in both data and computation that are particularly common for streaming data. To eliminate this limitation, this thesis holistically addresses computational intensity and dynamic data workload to achieve optimal data partitioning for scalable geospatial applications. Specifically, novel data partitioning algorithms have been developed to support scalable geospatial and temporal data management with new data models designed to represent dynamic data workload. Optimal partitions are realized by formulating a fine-grain spatial optimization problem that is solved using an evolutionary algorithm with spatially explicit operations. As an overarching approach to integrating the algorithms, data models and spatial optimization problem solving, GeoBalance is established as a workload-aware framework for supporting scalable cyberGIS (i.e. geographic information science and systems based on advanced cyberinfrastructure) analytics.","abstract_html":"Numerous applications and scientific domains have contributed to tremendous growth of geospatial data during the past several decades. To resolve the volume and velocity of such big data, distributed system approaches have been extensively studied to partition data for scalable analytics and associated applications. However, previous work on partitioning large geospatial data focuses on bulk-ingestion and static partitioning, hence is unable to handle dynamic variability in both data and computation that are particularly common for streaming data. To eliminate this limitation, this thesis holistically addresses computational intensity and dynamic data workload to achieve optimal data partitioning for scalable geospatial applications. Specifically, novel data partitioning algorithms have been developed to support scalable geospatial and temporal data management with new data models designed to represent dynamic data workload. Optimal partitions are realized by formulating a fine-grain spatial optimization problem that is solved using an evolutionary algorithm with spatially explicit operations. As an overarching approach to integrating the algorithms, data models and spatial optimization problem solving, GeoBalance is established as a workload-aware framework for supporting scalable cyberGIS (i.e. geographic information science and systems based on advanced cyberinfrastructure) analytics.","abstract_has_math":false,"creators":["Soltani, Kiumars"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Informatics","degree_department":null,"school":null,"contributors":["Wang, Shaowen","Han, Jiawei","Diesner, Jana","Parameswaran, Aditya"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02-06T19:36:05Z","date_published":"2019-02-06T19:36:05Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Distributed Computing, CyberGIS, Data-intensive Applications, Spatial Optimization, Data Partitioning"],"languages":["en"],"rights":["Copyright 2018 Kiumars Soltani"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102443","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Shaowen","Han, Jiawei","Diesner, Jana","Parameswaran, Aditya"]},{"key":"dc:creator","label":"Author","values":["Soltani, Kiumars"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-06T19:36:05Z","2018-11-28","2018-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Informatics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Distributed Computing, CyberGIS, Data-intensive Applications, Spatial Optimization, Data Partitioning"]}]},{"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 Kiumars Soltani"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102443"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Numerous applications and scientific domains have contributed to tremendous growth of geospatial data during the past several decades. To resolve the volume and velocity of such big data, distributed system approaches have been extensively studied to partition data for scalable analytics and associated applications. However, previous work on partitioning large geospatial data focuses on bulk-ingestion and static partitioning, hence is unable to handle dynamic variability in both data and computation that are particularly common for streaming data. To eliminate this limitation, this thesis holistically addresses computational intensity and dynamic data workload to achieve optimal data partitioning for scalable geospatial applications. Specifically, novel data partitioning algorithms have been developed to support scalable geospatial and temporal data management with new data models designed to represent dynamic data workload. Optimal partitions are realized by formulating a fine-grain spatial optimization problem that is solved using an evolutionary algorithm with spatially explicit operations. As an overarching approach to integrating the algorithms, data models and spatial optimization problem solving, GeoBalance is established as a workload-aware framework for supporting scalable cyberGIS (i.e. geographic information science and systems based on advanced cyberinfrastructure) analytics.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Kiumars Soltani, accepted the attached license on 2018-11-27 at 12:58.","The student, Kiumars Soltani, submitted this Dissertation for approval on 2018-11-27 at 13:13.","This Dissertation was approved for publication on 2018-11-28 at 12:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13122 on 2019-02-05 at 11:09:36","Made available in DSpace on 2019-02-06T19:36:05Z (GMT). No. of bitstreams: 2 SOLTANI-DISSERTATION-2018.pdf: 7825361 bytes, checksum: 4cfe859a3d7c2499dcf12fa4bc2c406f (MD5) LICENSE.txt: 4212 bytes, checksum: 5bba740e43a4d95931deb8690caf3e5b (MD5) Previous issue date: 2018-11-28"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A distributed workload-aware approach to partitioning geospatial big data for cybergis analytics"]}]}],"canonical_facts":{"dc:contributor":["Wang, Shaowen","Han, Jiawei","Diesner, Jana","Parameswaran, Aditya"],"dc:creator":["Soltani, Kiumars"],"dc:date":["2019-02-06T19:36:05Z","2018-11-28","2018-12"],"dc:description":["Numerous applications and scientific domains have contributed to tremendous growth of geospatial data during the past several decades. To resolve the volume and velocity of such big data, distributed system approaches have been extensively studied to partition data for scalable analytics and associated applications. However, previous work on partitioning large geospatial data focuses on bulk-ingestion and static partitioning, hence is unable to handle dynamic variability in both data and computation that are particularly common for streaming data. To eliminate this limitation, this thesis holistically addresses computational intensity and dynamic data workload to achieve optimal data partitioning for scalable geospatial applications. Specifically, novel data partitioning algorithms have been developed to support scalable geospatial and temporal data management with new data models designed to represent dynamic data workload. Optimal partitions are realized by formulating a fine-grain spatial optimization problem that is solved using an evolutionary algorithm with spatially explicit operations. As an overarching approach to integrating the algorithms, data models and spatial optimization problem solving, GeoBalance is established as a workload-aware framework for supporting scalable cyberGIS (i.e. geographic information science and systems based on advanced cyberinfrastructure) analytics.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Kiumars Soltani, accepted the attached license on 2018-11-27 at 12:58.","The student, Kiumars Soltani, submitted this Dissertation for approval on 2018-11-27 at 13:13.","This Dissertation was approved for publication on 2018-11-28 at 12:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13122 on 2019-02-05 at 11:09:36","Made available in DSpace on 2019-02-06T19:36:05Z (GMT). No. of bitstreams: 2 SOLTANI-DISSERTATION-2018.pdf: 7825361 bytes, checksum: 4cfe859a3d7c2499dcf12fa4bc2c406f (MD5) LICENSE.txt: 4212 bytes, checksum: 5bba740e43a4d95931deb8690caf3e5b (MD5) Previous issue date: 2018-11-28"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/102443"],"dc:language":["en"],"dc:rights":["Copyright 2018 Kiumars Soltani"],"dc:subject":["Distributed Computing, CyberGIS, Data-intensive Applications, Spatial Optimization, Data Partitioning"],"dc:title":["A distributed workload-aware approach to partitioning geospatial big data for cybergis analytics"],"dc:type":["text"],"thesis:degree_discipline":["Informatics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:40Z"}