{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124567"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124567","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Accelerating queries for structured and unstructured data","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Jin, Tengjun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Kang, Daniel"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-04-30","date_published":"2024-04-30","updated_at":"2026-07-22T22:25:02Z","subjects":["Database Systems","Machine Learning","Approximate Query Processing"],"languages":["en","eng"],"rights":["Copyright 2024 Tengjun Jin"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124567","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kang, Daniel"]},{"key":"dc:creator","label":"Author","values":["Jin, Tengjun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-04-30","2024-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Database Systems","Machine Learning","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 2024 Tengjun Jin"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124567"]}]},{"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 2026-05-01","The student, Tengjun Jin, accepted the attached license on 2024-04-23 at 14:19.","The student, Tengjun Jin, submitted this Thesis for approval on 2024-04-23 at 16:17.","This Thesis was approved for publication on 2024-04-30 at 14:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20584 on 2024-09-16 at 00:44:27","Data analytics is important for making data-driven decisions. As data volumes expand, the efficiency and cost of executing queries become critical concerns for analysts. Traditionally, analytics systems have prioritized structured data. Approximate Query Processing (AQP) systems, which provide faster aggregation queries by delivering approximate results, have been developed to enhance efficiency. However, they are limited used in real-application due to compatibility issues with popular databases and restrictions on the types of queries they can handle. To overcome these limitations, we have designed an innovative AQP system that functions as middleware. This system uses online sampling techniques to accelerate aggregation queries and can meet user-specified error targets. With advancements in machine learning (ML), analysts are increasingly interested in analyzing unstructured data (videos, images, text, and audio) to extract semantic information. Current analytics systems typically integrate ML models through user-defined functions (UDFs). These UDFs can be difficult to optimize and require application users to write complex, nested table expressions. To address these challenges, we introduce a new data model, AIDM, enabling users to query ML model outputs as standard SQL tables, through virtual columns and virtual tables. We implement AIDM, as well as novel optimizations for accelerating both approximate and exact queries in AIDB. Our evaluations show that the AQP system can provide speedups of up to 87x and AIDB can reduce the number of ML model invocations by up to 98%."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Accelerating queries for structured and unstructured data"]}]}],"canonical_facts":{"dc:contributor":["Kang, Daniel"],"dc:creator":["Jin, Tengjun"],"dc:date":["2024-04-30","2024-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01","The student, Tengjun Jin, accepted the attached license on 2024-04-23 at 14:19.","The student, Tengjun Jin, submitted this Thesis for approval on 2024-04-23 at 16:17.","This Thesis was approved for publication on 2024-04-30 at 14:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20584 on 2024-09-16 at 00:44:27","Data analytics is important for making data-driven decisions. As data volumes expand, the efficiency and cost of executing queries become critical concerns for analysts. Traditionally, analytics systems have prioritized structured data. Approximate Query Processing (AQP) systems, which provide faster aggregation queries by delivering approximate results, have been developed to enhance efficiency. However, they are limited used in real-application due to compatibility issues with popular databases and restrictions on the types of queries they can handle. To overcome these limitations, we have designed an innovative AQP system that functions as middleware. This system uses online sampling techniques to accelerate aggregation queries and can meet user-specified error targets. With advancements in machine learning (ML), analysts are increasingly interested in analyzing unstructured data (videos, images, text, and audio) to extract semantic information. Current analytics systems typically integrate ML models through user-defined functions (UDFs). These UDFs can be difficult to optimize and require application users to write complex, nested table expressions. To address these challenges, we introduce a new data model, AIDM, enabling users to query ML model outputs as standard SQL tables, through virtual columns and virtual tables. We implement AIDM, as well as novel optimizations for accelerating both approximate and exact queries in AIDB. Our evaluations show that the AQP system can provide speedups of up to 87x and AIDB can reduce the number of ML model invocations by up to 98%."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124567"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Tengjun Jin"],"dc:subject":["Database Systems","Machine Learning","Approximate Query Processing"],"dc:title":["Accelerating queries for structured and unstructured data"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}