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Enhancing Stream Reasoning by Modeling the Importance of the Streaming Data
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Enhancing Stream Reasoning by Modeling the Importance of the Streaming Data. |
| 개인저자 | Yan, Rui. |
| 단체저자명 | Rensselaer Polytechnic Institute. Computer Science. |
| 발행사항 | [S.l.]: Rensselaer Polytechnic Institute., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 184 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438206533 |
| 학위논문주기 | Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Deborah L. McGuinness. |
| 요약 | The requirement to extract the hidden information out of the data stream is rising, however, traditional stream processing systems cannot meet this requirement as they are not designed to do so. This gives birth to the new research domain of str |
| 요약 | Streaming data is boundless, enormous, and heterogeneous, which adds extra dimensions to the challenges of realizing the vision of stream reasoning, in addition to temporal constraints. A widely-adopted way to process the streams is via leveragi |
| 요약 | Streaming data intrinsically has many different orderings, such as temporarily, precision, provenance, and trust, etc. If diverse data orderings can be utilized to model the data importance, stream reasoning can be benefited by being data-discri |
| 요약 | Generally speaking, this dissertation delivers a conceptual model, and a set of infrastructure that can facilitate its general application in stream reasoning. Specifically, the first contribution is an innovative notion of semantic importance. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
