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dc.contributor.authorVenkatesan M.
dc.contributor.authorPrabhavathy P.
dc.date.accessioned2020-03-31T14:15:21Z-
dc.date.available2020-03-31T14:15:21Z-
dc.date.issued2018
dc.identifier.citationBig Data Analytics for Satellite Image Processing and Remote Sensing, 2018, Vol., pp.22-33en_US
dc.identifier.uri10.4018/978-1-5225-3643-7.ch002
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/13762-
dc.description.abstractEffective and efficient strategies to acquire, manage, and analyze data leads to better decision making and competitive advantage. The development of cloud computing and the big data era brings up challenges to traditional data mining algorithms. The processing capacity, architecture, and algorithms of traditional database systems are not coping with big data analysis. Big data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences, and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this chapter is to propose a multi-ranking decision tree big data approach to handle complex spatial landslide data. The proposed classifier performance is validated with massive real-time dataset. The results indicate that the classifier exhibits both time efficiency and scalability. © 2018, IGI Global. All rights reserved.en_US
dc.titleBig data computation model for landslide risk analysis using remote sensing dataen_US
dc.typeBook Chapteren_US
Appears in Collections:3. Book Chapters

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