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020 ▼a 9780438206533
035 ▼a (MiAaPQ)AAI10786766
035 ▼a (MiAaPQ)rpi:11266
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Yan, Rui.
24510 ▼a Enhancing Stream Reasoning by Modeling the Importance of the Streaming Data.
260 ▼a [S.l.] : ▼b Rensselaer Polytechnic Institute., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 184 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Deborah L. McGuinness.
5021 ▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018.
520 ▼a 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
520 ▼a 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
520 ▼a 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
520 ▼a 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.
590 ▼a School code: 0185.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a Rensselaer Polytechnic Institute. ▼b Computer Science.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0185
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997364 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자