Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Tak-Lon Wu"'
Autor:
Clayton A. Davis, Giovanni Luca Ciampaglia, Luca Maria Aiello, Keychul Chung, Michael D. Conover, Emilio Ferrara, Alessandro Flammini, Geoffrey C. Fox, Xiaoming Gao, Bruno Gonçalves, Przemyslaw A. Grabowicz, Kibeom Hong, Pik-Mai Hui, Scott McCaulay, Karissa McKelvey, Mark R. Meiss, Snehal Patil, Chathuri Peli Kankanamalage, Valentin Pentchev, Judy Qiu, Jacob Ratkiewicz, Alex Rudnick, Benjamin Serrette, Prashant Shiralkar, Onur Varol, Lilian Weng, Tak-Lon Wu, Andrew J. Younge, Filippo Menczer
Publikováno v:
PeerJ Computer Science, Vol 2, p e87 (2016)
The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observ
Externí odkaz:
https://doaj.org/article/83786ca3295e4ac98893ddc06538a76b
Publikováno v:
Future Generation Computer Systems. 29:1035-1048
Recent advances in data-intensive computing for science discovery are fueling a dramatic growth in the use of data-intensive iterative computations. The utility computing model introduced by cloud computing, combined with the rich set of cloud infras
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e5277cd576c9a05d9e6676707cf175da
https://doi.org/10.1016/j.future.2012.05.027
https://doi.org/10.1016/j.future.2012.05.027
Publikováno v:
Concurrency and Computation: Practice and Experience. 23:2338-2354
Cloud computing offers exciting new approaches for scientific computing that leverage major commercial players’ hardware and software investments in large-scale data centers. Loosely coupled problems are very important in many scientific fields, an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c026fdec02c8a24c61b1d4daf7d91873
https://doi.org/10.1002/cpe.1780
https://doi.org/10.1002/cpe.1780
Autor:
Sherif Abdelhamid, Maksudul Alam, Richard Alo, Shaikh Arifuzzaman, Pete Beckman, Tirtha Bhattacharjee, Hasanuzzaman Bhuiyan, Keith Bisset, Stephen Eubank, Albert C. Esterline, Edward A. Fox, Geoffrey C. Fox, S. M. Shamimul Hasan, Harshal Hayatnagarkar, Maleq Khan, Chris J. Kuhlman, Madhav V. Marathe, Natarajan Meghanathan, Henning S. Mortveit, Judy Qiu, S.S. Ravi, Zalia Shams, Ongard Sirisaengtaksin, Samarth Swarup, Anil Kumar S. Vullikanti, Tak-Lon Wu
Publikováno v:
2014 IEEE 10th International Conference on e-Science.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::673c6ec8dbaf5a48f9fe0885ecc8cdc9
https://doi.org/10.1109/escience.2014.21
https://doi.org/10.1109/escience.2014.21
Publikováno v:
UCC
Recent advancements in data intensive computing for science discovery are fueling a dramatic growth in use of data-intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrast
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46b4a3d1bbadcb58fceb414029a6a3ae
https://doi.org/10.1109/ucc.2011.23
https://doi.org/10.1109/ucc.2011.23
Autor:
Judy Qiu, Hui Li, Seung-Hee Bae, Thilina Gunarathne, Saliya Ekanayake, Tak-Lon Wu, Geoffrey C. Fox, Adam Hughes, Bingjing Zhang, Jong Youl Choi, Yang Ruan, Jaliya Ekanayake
Publikováno v:
BMC Bioinformatics
Background Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapRe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c37a688dbc1ad95008b6c1722a2156b
https://doi.org/10.1186/1471-2105-11-s12-s3
https://doi.org/10.1186/1471-2105-11-s12-s3
Publikováno v:
CloudCom
The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure services offers a very viable alternative to traditional servers and computing clusters. MapReduce distributed data processing architecture h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96d99d3686bdcd69c2aed54e6250b037
https://doi.org/10.1109/cloudcom.2010.107
https://doi.org/10.1109/cloudcom.2010.107
Publikováno v:
CloudCom
Many scientific applications suffer from the lack of a unified approach to support the management and efficient processing of large-scale data. The Twister MapReduce Framework, which not only supports the traditional MapReduce programming model but a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0709c58bd910adfe6aefdf611381f63c
https://doi.org/10.1109/cloudcom.2010.37
https://doi.org/10.1109/cloudcom.2010.37
Publikováno v:
HPDC
Cloud computing offers exciting new approaches for scientific computing that leverages the hardware and software investments on large scale data centers by major commercial players. Loosely coupled problems are very important in many scientific field
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::674fa521fdb3a438a083760d3130fcb4
https://doi.org/10.1145/1851476.1851544
https://doi.org/10.1145/1851476.1851544
Publikováno v:
2011 Fourth IEEE International Conference on Utility & Cloud Computing (UCC); 2011, p97-104, 8p