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10.1109/BigData.2014.7004353 http://scihub22266oqcxt.onion/10.1109/BigData.2014.7004353 C4388237!4388237!25859564     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25859564&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25859564.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Proc+IEEE+Int+Conf+Big+Data 2014 ; 2014 (ä): 29-35 Nephropedia Template TP {{tp|p=25859564|t=2014. Towards Scalable Graph Computation on Mobile Devices |pdf=|usr=}}{{25859564}} gab.com Text Towards Scalable Graph Computation on Mobile Devices JO: Proc IEEE Int Conf Big Data http://www.kidney.de/pget.php?&tw=1&pmid=25859564 #FOAvip Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13? Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach. Twit Text FOAVip Towards Scalable Graph Computation on Mobile Devices ????Proc IEEE Int Conf Big Data http://www.kidney.de/pget.php?&tw=1&pmid=25859564 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25859564 #FOAvip English Wikipedia <ref>{{Cite pmid|25859564}}</ref> Towards Scalable Graph Computation on Mobile Devices #MMPMID25859564 Chen Y; Lin Z; Pienta R; Kahng M; Chau DH Proc IEEE Int Conf Big Data 2014[Oct]; 2014 (ä): 29-35 PMID25859564show ga Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use a single mobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiar memory mapping capability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as 272 million edges (Google+ social graph), at a speed that is only a few times slower than a 13? Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach. äTowards Scalable Graph Computation on Mobile Devices (epmc).pdf DeepDyve Pubget Overpricing