Hadoop Performance Modeling for LWLR
(Sprache: Englisch)
Big Abstracts processing, as the advice comes from multiple, heterogeneous, free sources with circuitous and evolving relationships, and keeps growing. Big abstracts is difficult to plan with appliance a lot of relational database administration systems and...
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Big Abstracts processing, as the advice comes from multiple, heterogeneous, free sources with circuitous and evolving relationships, and keeps growing. Big abstracts is difficult to plan with appliance a lot of relational database administration systems and desktop statistics and accommodation packages. The proposed shows a Big Abstracts processing model, from the abstracts mining perspective. This data-driven archetypal involves demand-driven accession of adevice sources, mining and analysis, user absorption modelling, and aegis and aloofness considerations. We assay the arduous issues in the data-driven archetypal and aswell in the Big abstracts revolution. We proposed a new allocation arrangement which can finer advance the allocation achievement in the bearings that training abstracts is available.
Autoren-Porträt von Komuravelli Mounika, N. Naveen Kumar
Mounika, KomuravelliKomuravelli Mounika studied M.Tech in the Department of Computer Science, School of Information Technology, JNTUH, Hyderabad, Telangana.
Bibliographische Angaben
- Autoren: Komuravelli Mounika , N. Naveen Kumar
- 2020, 52 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 6139946166
- ISBN-13: 9786139946167
Sprache:
Englisch
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