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Titlebook: Big Data Analytics; Third International Srinath Srinivasa,Sameep Mehta Conference proceedings 2014 Springer International Publishing Switz

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31#
發(fā)表于 2025-3-26 21:27:21 | 只看該作者
HDR-IORT: Physics and Techniquesto address other aspects for the multi-site enterprise such as optimal routing of print jobs and capacity planning. This paper presents a system that can assist the enterprise manager to make efficient job routing decisions within the enterprise. The system described is part of the LDP suite enabling multi-site enterprise print management.
32#
發(fā)表于 2025-3-27 05:05:54 | 只看該作者
33#
發(fā)表于 2025-3-27 06:42:47 | 只看該作者
34#
發(fā)表于 2025-3-27 12:02:55 | 只看該作者
Remote Interactive Visualization of Parallel Implementation of Structural Feature Extraction of Thrfeatures of point cloud. Our work includes: (a) parallel implementation a topology-based algorithm for extraction of structural features from lidar point cloud using GPGPU (General Purpose Graphics Processing Unit) computing, and (b) using a LAN (Local Area Network)-based server-client architecture to achieve remote visualization.
35#
發(fā)表于 2025-3-27 14:59:46 | 只看該作者
Conference proceedings 2014dia, in December 2014. The 11 revised full papers and 6 short papers were carefully reviewed and selected from 35 submissions and cover topics on media analytics; geospatial big data; semantics and data models; search and retrieval; graphics and visualization; application-specific big data.
36#
發(fā)表于 2025-3-27 21:40:26 | 只看該作者
37#
發(fā)表于 2025-3-28 01:39:28 | 只看該作者
38#
發(fā)表于 2025-3-28 05:25:40 | 只看該作者
Sentiment Analysis of Twitter Feeds,ative and neutral. In particular, we investigate the relevance of using a two-step classifier and negation detection in the space of Twitter Sentiment analysis. An efficient sentiment analyzer is deemed to be a must in the era of big data where preponderance of electronic communication is a major bo
39#
發(fā)表于 2025-3-28 07:47:29 | 只看該作者
Graphical Analysis and Visualization of Big Data in Business Domains, Data is actually characterized by the 4V’s – Volume, Variety, Velocity and Veracity. We propose a Big Data Stack and analytics solution that particularly caters to this important problem of addressing Variety and Velocity aspects of data by exploiting inherent relationship among data elements. A un
40#
發(fā)表于 2025-3-28 13:20:52 | 只看該作者
Processing Spatio-temporal Data On Map-Reduce,orm, supporting efficient analytics operations over such voluminous datasets become prohibitively expensive. As a result there has been an increasing interest in using map-reduce to parallelize the processing of large-scale spatio-temporal data. While Hadoop, which has become the de-facto implementa
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