소장자료
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| 008 | 250123s2024 us ||||||||||||||c||eng d▲ | ||
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| 100 | 1 | ▼aHe, Wenjia.▲ | |
| 245 | 1 | 0 | ▼aTowards Query Processing in Video Database Management Systems▼h[electronic resource].▲ |
| 260 | ▼a[S.l.]: ▼bUniversity of Michigan. ▼c2024▲ | ||
| 260 | 1 | ▼aAnn Arbor : ▼bProQuest Dissertations & Theses, ▼c2024▲ | |
| 300 | ▼a1 online resource(202 p.)▲ | ||
| 500 | ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.▲ | ||
| 500 | ▼aAdvisor: Cafarella, Michael;Jagadish, H. V.▲ | ||
| 502 | 1 | ▼aThesis (Ph.D.)--University of Michigan, 2024.▲ | |
| 520 | ▼aVideos have been widely adopted across various applications, highlighting the increasing importance of database management systems that can support video queries. However, achieving effective query processing in video database management systems is challenging due to factors such as the substantial size of video databases and the unstructured nature of video content. To address these challenges, I demonstrate that video-specific algorithms that support query processing in video database management systems are essential for performance (accelerating video selection queries), policy (balancing competing query requirements), and explanation (supporting queries for real-world explanation). To support this statement, my dissertation focuses on four key parts: (1) building a new indexing mechanism that captures visual similarity for filtering items that are likely to satisfy the query predicate, (2) developing a video degradation-accuracy profiling system, helping administrators to choose an appropriate degradation setting for competing requirement trade-off in video analytics, (3) proposing a commonsense knowledge-enhanced indexing method, which initially constructs a lossy but inexpensive index and subsequently patches it to quickly identify query result candidates, and (4) implementing a causal inference system that uncovers confounding variables within images to solve confounding bias and compute more accurate average treatment effects (ATE).▲ | ||
| 590 | ▼aSchool code: 0127.▲ | ||
| 650 | 4 | ▼aComputer science.▲ | |
| 650 | 4 | ▼aComputer engineering.▲ | |
| 650 | 4 | ▼aEngineering.▲ | |
| 653 | ▼aDatabase▲ | ||
| 653 | ▼aQuery processing▲ | ||
| 653 | ▼aVideo analytics▲ | ||
| 653 | ▼aQuery optimization▲ | ||
| 690 | ▼a0984▲ | ||
| 690 | ▼a0464▲ | ||
| 690 | ▼a0800▲ | ||
| 690 | ▼a0537▲ | ||
| 710 | 2 | 0 | ▼aUniversity of Michigan.▼bComputer Science & Engineering.▲ |
| 773 | 0 | ▼tDissertations Abstracts International▼g85-12B.▲ | |
| 790 | ▼a0127▲ | ||
| 791 | ▼aPh.D.▲ | ||
| 792 | ▼a2024▲ | ||
| 793 | ▼aEnglish▲ | ||
| 856 | 4 | 0 | ▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162865▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.▲ |
Towards Query Processing in Video Database Management Systems[electronic resource]
자료유형
국외eBook
서명/책임사항
Towards Query Processing in Video Database Management Systems [electronic resource].
개인저자
발행사항
[S.l.] : University of Michigan. 2024 Ann Arbor : ProQuest Dissertations & Theses , 2024
형태사항
1 online resource(202 p.)
일반주기
Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Advisor: Cafarella, Michael;Jagadish, H. V.
Advisor: Cafarella, Michael;Jagadish, H. V.
학위논문주기
Thesis (Ph.D.)--University of Michigan, 2024.
요약주기
Videos have been widely adopted across various applications, highlighting the increasing importance of database management systems that can support video queries. However, achieving effective query processing in video database management systems is challenging due to factors such as the substantial size of video databases and the unstructured nature of video content. To address these challenges, I demonstrate that video-specific algorithms that support query processing in video database management systems are essential for performance (accelerating video selection queries), policy (balancing competing query requirements), and explanation (supporting queries for real-world explanation). To support this statement, my dissertation focuses on four key parts: (1) building a new indexing mechanism that captures visual similarity for filtering items that are likely to satisfy the query predicate, (2) developing a video degradation-accuracy profiling system, helping administrators to choose an appropriate degradation setting for competing requirement trade-off in video analytics, (3) proposing a commonsense knowledge-enhanced indexing method, which initially constructs a lossy but inexpensive index and subsequently patches it to quickly identify query result candidates, and (4) implementing a causal inference system that uncovers confounding variables within images to solve confounding bias and compute more accurate average treatment effects (ATE).
주제
ISBN
9798382741062
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