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Guided Data Fusion
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Guided Data Fusion. |
| 개인저자 | Pradhan, Romila. |
| 단체저자명 | Purdue University. Computer Sciences. |
| 발행사항 | [S.l.]: Purdue University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 126 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438369047 |
| 학위논문주기 | Thesis (Ph.D.)--Purdue University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Sunil Prabhakar. |
| 요약 | While the volume and variety of data furnished by disparate data sources has rocketed over the years, often there is little to no restraint over the quality of data available on the Internet |
| 요약 | Recent years have witnessed a number of data fusion systems that propose solutions to consolidate multiple instances of a data item, distinguish correct from incorrect information and present a unified, consistent and meaningful record to users. |
| 요약 | The first challenge relates to integrating feedback from users to rapidly resolve conflicts. The objective is to effectively and efficiently integrate user feedback for maximum benefit to data fusion. For this purpose, we develop a novel framewo |
| 요약 | The second challenge relates to leveraging relations between claims of data items to identify multiple related correct claims. The objective is to recognize existing entity-relationships among claims and integrate them with data fusion systems t |
| 요약 | Our experimental evaluations using real-world and synthetic datasets demonstrate the effectiveness and efficiency of our proposed approaches to improve conflict resolution of data integrated from multiple sources. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
