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Leveraging Data Semantics for Relational Data Management Tasks
Leveraging Data Semantics for Relational Data Management Tasks
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103645
- ISBN
- 9798314874769
- DDC
- 004
- 저자명
- Xing, Junjie.
- 서명/저자
- Leveraging Data Semantics for Relational Data Management Tasks
- 발행사항
- [Sl] : University of Michigan, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 121 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Jagadish, Hosagrahar V.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 초록/해제
- 요약In an era of rapidly growing data, efficient and intelligent relational data management is essential for generating actionable insights and automating decision-making. A key factor driving advancements in this domain is the use of data semantics, which captures the deeper meaning and context of data, extending beyond traditional heuristic and syntactic approaches. By leveraging data semantics, we can enhance tasks such as insight generation, data integration, and other essential relational data management tasks. This dissertation explores how advanced data semantics can address several key challenges in relational data management. First, we investigate methods to capture user-defined semantics for assessing the interestingness of data insights, moving beyond traditional developer-defined measures of interestingness. Second, we leverage the enhanced natural language understanding capabilities of large language models (LLMs) to generate fine-grained column semantics for relational data and introduce the concept of "aggregate-related table search", which captures table semantics across varying aggregation levels. Finally, we propose a self-training framework for LLM fine-tuning on table-related tasks, incorporating table task semantics by generating and validating training data to improve model performance in tasks such as natural language to SQL and schema matching. Through these contributions, this dissertation aims to advance relational data management by embedding a deeper understanding of different aspects of data semantics into various data applications, including data analysis and data discovery systems, ultimately improving the performance of relational data management tasks.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 일반주제명
- Systems science
- 키워드
- Data semantics
- 키워드
- Data exploration
- 키워드
- Schema matching
- 기타저자
- University of Michigan Computer Science & Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798314874769
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aXing, Junjie.
■24510▼aLeveraging Data Semantics for Relational Data Management Tasks
■260 ▼a[Sl]▼bUniversity of Michigan▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a121 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Jagadish, Hosagrahar V.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aIn an era of rapidly growing data, efficient and intelligent relational data management is essential for generating actionable insights and automating decision-making. A key factor driving advancements in this domain is the use of data semantics, which captures the deeper meaning and context of data, extending beyond traditional heuristic and syntactic approaches. By leveraging data semantics, we can enhance tasks such as insight generation, data integration, and other essential relational data management tasks. This dissertation explores how advanced data semantics can address several key challenges in relational data management. First, we investigate methods to capture user-defined semantics for assessing the interestingness of data insights, moving beyond traditional developer-defined measures of interestingness. Second, we leverage the enhanced natural language understanding capabilities of large language models (LLMs) to generate fine-grained column semantics for relational data and introduce the concept of "aggregate-related table search", which captures table semantics across varying aggregation levels. Finally, we propose a self-training framework for LLM fine-tuning on table-related tasks, incorporating table task semantics by generating and validating training data to improve model performance in tasks such as natural language to SQL and schema matching. Through these contributions, this dissertation aims to advance relational data management by embedding a deeper understanding of different aspects of data semantics into various data applications, including data analysis and data discovery systems, ultimately improving the performance of relational data management tasks.
■590 ▼aSchool code: 0127.
■650 4▼aComputer science
■650 4▼aComputer engineering
■650 4▼aSystems science
■653 ▼aData semantics
■653 ▼aRelational data management tasks
■653 ▼aLarge language models
■653 ▼aData exploration
■653 ▼aSchema matching
■690 ▼a0984
■690 ▼a0464
■690 ▼a0790
■71020▼aUniversity of Michigan▼bComputer Science & Engineering.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358107▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
