{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115434"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115434","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Overcoming barriers in data warehouse replatforming","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_has_math":false,"creators":["Aleyasen, Amirhossein"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Winslett, Marianne","Park, Yongjoo","Alawini, Abdussalam","Ludäscher, Bertram","Antova, Lyublena"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["Workload Analysis","Data Warehousing","Porting Complexity","Database Replatforming","Adaptive Data Virtualization","Data Replication","Query Routing","Cloud Migration"],"languages":["en","eng"],"rights":["Copyright 2022 Amirhossein Aleyasen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115434","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Winslett, Marianne","Park, Yongjoo","Alawini, Abdussalam","Ludäscher, Bertram","Antova, Lyublena"]},{"key":"dc:creator","label":"Author","values":["Aleyasen, Amirhossein"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-22"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["Workload Analysis","Data Warehousing","Porting Complexity","Database Replatforming","Adaptive Data Virtualization","Data Replication","Query Routing","Cloud Migration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Amirhossein Aleyasen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115434"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Amirhossein Aleyasen, accepted the attached license on 2022-04-21 at 19:08.","The student, Amirhossein Aleyasen, submitted this Dissertation for approval on 2022-04-21 at 19:22.","This Dissertation was approved for publication on 2022-04-22 at 14:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17904 on 2022-11-11 at 13:42:54","With the development of data-warehouse-as-a-service systems, an increasing number of companies have opted to migrate from on-premises warehouse systems to cloud-native systems. In spite of this, however, replatforming data warehouses poses significant challenges, due to the syntactic, semantic, functional, and performance differences between data warehousing vendors, such as differences in SQL dialects. In recent years, adaptive data virtualization was proposed as a way to reduce the costs associated with data warehouse migration, by keeping both the network and application layers intact and translating queries in real time between the old and new vendors' SQL dialects. Although adaptive data virtualization greatly reduces the cost of data warehouse replatforming, it still poses several challenges. One of the main challenges is an incomplete understanding of characteristics, needs, and behavior of existing workloads in traditional data warehouses. When moving from an on-premises data warehouse to a cloud-based data warehouse, run-time performance can be negatively impacted, even when there is sufficient understanding and analysis of the workload. This dissertation takes a step towards addressing the aforementioned challenges in adaptive data virtualization, by designing and demonstrating a workload analyzer that provides users with in-depth insights into their applications, users, data volume and data variety, logical and physical designs, tables, views, queries, performance, batch load behavior, and ETL processing. We use this workload analyzer to characterize 40 data warehouse workloads from some of the world’s largest enterprises, and highlight the unexpected findings and practical takeaways. We leverage the workload analyzer to design and evaluate a porting complexity model that informs users of the cost of replatforming and prioritize applications for replatforming projects. Deployment and evaluation of the porting complexity model shows that companies and experts find the information useful when they are considering migrating their data to a cloud data warehouse. Finally, to address cloud data warehouse throughput concerns for applications using adaptive data virtualization, we design, implement and evaluate a middleware-based database replication solution that combines the strengths of existing data replication methods while mitigating their limitations. Extensive experiments on real-world and benchmark workloads show the practicality and efficiency of the proposed solution."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Overcoming barriers in data warehouse replatforming"]}]}],"canonical_facts":{"dc:contributor":["Winslett, Marianne","Park, Yongjoo","Alawini, Abdussalam","Ludäscher, Bertram","Antova, Lyublena"],"dc:creator":["Aleyasen, Amirhossein"],"dc:date":["2022-05","2022-04-22"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Amirhossein Aleyasen, accepted the attached license on 2022-04-21 at 19:08.","The student, Amirhossein Aleyasen, submitted this Dissertation for approval on 2022-04-21 at 19:22.","This Dissertation was approved for publication on 2022-04-22 at 14:44.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17904 on 2022-11-11 at 13:42:54","With the development of data-warehouse-as-a-service systems, an increasing number of companies have opted to migrate from on-premises warehouse systems to cloud-native systems. In spite of this, however, replatforming data warehouses poses significant challenges, due to the syntactic, semantic, functional, and performance differences between data warehousing vendors, such as differences in SQL dialects. In recent years, adaptive data virtualization was proposed as a way to reduce the costs associated with data warehouse migration, by keeping both the network and application layers intact and translating queries in real time between the old and new vendors' SQL dialects. Although adaptive data virtualization greatly reduces the cost of data warehouse replatforming, it still poses several challenges. One of the main challenges is an incomplete understanding of characteristics, needs, and behavior of existing workloads in traditional data warehouses. When moving from an on-premises data warehouse to a cloud-based data warehouse, run-time performance can be negatively impacted, even when there is sufficient understanding and analysis of the workload. This dissertation takes a step towards addressing the aforementioned challenges in adaptive data virtualization, by designing and demonstrating a workload analyzer that provides users with in-depth insights into their applications, users, data volume and data variety, logical and physical designs, tables, views, queries, performance, batch load behavior, and ETL processing. We use this workload analyzer to characterize 40 data warehouse workloads from some of the world’s largest enterprises, and highlight the unexpected findings and practical takeaways. We leverage the workload analyzer to design and evaluate a porting complexity model that informs users of the cost of replatforming and prioritize applications for replatforming projects. Deployment and evaluation of the porting complexity model shows that companies and experts find the information useful when they are considering migrating their data to a cloud data warehouse. Finally, to address cloud data warehouse throughput concerns for applications using adaptive data virtualization, we design, implement and evaluate a middleware-based database replication solution that combines the strengths of existing data replication methods while mitigating their limitations. Extensive experiments on real-world and benchmark workloads show the practicality and efficiency of the proposed solution."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115434"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Amirhossein Aleyasen"],"dc:subject":["Workload Analysis","Data Warehousing","Porting Complexity","Database Replatforming","Adaptive Data Virtualization","Data Replication","Query Routing","Cloud Migration"],"dc:title":["Overcoming barriers in data warehouse replatforming"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}