{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117660"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117660","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"DeepOLA: An online aggregation approach to approximating deeply nested queries","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Sheoran, Nikhil"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Park, Yongjoo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Online Aggregation","Approximate Query Processing"],"languages":["en","eng"],"rights":["Copyright 2022 Nikhil Sheoran"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117660","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Park, Yongjoo"]},{"key":"dc:creator","label":"Author","values":["Sheoran, Nikhil"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-05"]},{"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":["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":["Online Aggregation","Approximate Query Processing"]}]},{"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 Nikhil Sheoran"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117660"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Nikhil Sheoran, accepted the attached license on 2022-12-02 at 17:32.","The student, Nikhil Sheoran, submitted this Thesis for approval on 2022-12-02 at 17:40.","This Thesis was approved for publication on 2022-12-05 at 11:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18633 on 2023-04-12 at 08:11:37","For exploratory data analysis, it is often desirable to know what answers you are likely to get before actually obtaining those answers. This can potentially be achieved by designing systems to offer the estimates of a data operation result — say op(data) — earlier in the process based on partial data processing. Those estimates continuously refine as more data is processed and finally converge to the exact answer. Unfortunately, the existing techniques — called Online Aggregation (OLA) — are limited to a single operation; that is, we cannot obtain the estimates for op(op(data)) or op(...(op(data))). If this Deep OLA becomes possible, data analysts will be able to explore data more interactively using complex cascade operations. In this work, we take a step toward Deep OLA with evolving data frames (edf), a novel data model to offer OLA for nested ops — op(...(op(data))) — by representing an evolving structured data (with converging estimates) that is closed under set operations. That is, op(edf) produces yet another edf; thus, we can freely apply successive operations to edf and obtain an OLA output for each op. We evaluate its viability with DeepOLA, an edf-based OLA system, by examining against state-of-the-art OLA and non-OLA systems. In our experiments on TPC-H dataset, DeepOLA produces its first estimates 12.8× faster (median) — with 1.3× median slowdown for exact answers — compared to conventional systems, while even producing faster exact results for some queries (i.e. no overhead). Besides its generality, DeepOLA is also 1.92× faster (median) than existing OLA systems in producing estimates of under 1% relative errors."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["DeepOLA: An online aggregation approach to approximating deeply nested queries"]}]}],"canonical_facts":{"dc:contributor":["Park, Yongjoo"],"dc:creator":["Sheoran, Nikhil"],"dc:date":["2022-12","2022-12-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Nikhil Sheoran, accepted the attached license on 2022-12-02 at 17:32.","The student, Nikhil Sheoran, submitted this Thesis for approval on 2022-12-02 at 17:40.","This Thesis was approved for publication on 2022-12-05 at 11:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18633 on 2023-04-12 at 08:11:37","For exploratory data analysis, it is often desirable to know what answers you are likely to get before actually obtaining those answers. This can potentially be achieved by designing systems to offer the estimates of a data operation result — say op(data) — earlier in the process based on partial data processing. Those estimates continuously refine as more data is processed and finally converge to the exact answer. Unfortunately, the existing techniques — called Online Aggregation (OLA) — are limited to a single operation; that is, we cannot obtain the estimates for op(op(data)) or op(...(op(data))). If this Deep OLA becomes possible, data analysts will be able to explore data more interactively using complex cascade operations. In this work, we take a step toward Deep OLA with evolving data frames (edf), a novel data model to offer OLA for nested ops — op(...(op(data))) — by representing an evolving structured data (with converging estimates) that is closed under set operations. That is, op(edf) produces yet another edf; thus, we can freely apply successive operations to edf and obtain an OLA output for each op. We evaluate its viability with DeepOLA, an edf-based OLA system, by examining against state-of-the-art OLA and non-OLA systems. In our experiments on TPC-H dataset, DeepOLA produces its first estimates 12.8× faster (median) — with 1.3× median slowdown for exact answers — compared to conventional systems, while even producing faster exact results for some queries (i.e. no overhead). Besides its generality, DeepOLA is also 1.92× faster (median) than existing OLA systems in producing estimates of under 1% relative errors."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117660"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Nikhil Sheoran"],"dc:subject":["Online Aggregation","Approximate Query Processing"],"dc:title":["DeepOLA: An online aggregation approach to approximating deeply nested queries"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}