DocumentCode :
3301919
Title :
Construct rough approximation based on GAE
Author :
Lin Shi ; Jun Meng ; Yang Zhou ; Tsauyoung Lin
Author_Institution :
Sch. of Comput. Sci. & Technol., Dalian Univ. of Technol., Dalian, China
fYear :
2013
fDate :
13-15 Dec. 2013
Firstpage :
259
Lastpage :
264
Abstract :
Recently cloud computing has emerged as a new paradigm which focuses on web-scale problems, large data centers, multiple models of computing and highly-interactive web applications. It is high available and scalable for distributed and parallel data storage and computing based on a large amount of cheap PCs. As the representative product, Google app engine (GAE), which acts a platform as a service (PaaS) cloud computing platform, mainly contains Google File System (GFS) and MapReduce programming model for massive data process. This paper analyses GAE from the point of Granular computing (GrC) and explain why it is suitable for massive data mining. Further we present an example of how to use it to construct neighborhoods of rough set and compute lower and upper approximations accurately and strictly.
Keywords :
approximation theory; cloud computing; data mining; granular computing; rough set theory; GAE; GFS; Google File System; Google app engine; GrC; MapReduce programming model; PaaS cloud computing platform; construct rough approximation; granular computing; lower approximations; massive data mining; platform as a service; rough set; upper approximations; Approximation algorithms; Approximation methods; Computational modeling; Data models; Educational institutions; Google; Programming; Clouding computing; Google app engine; Granular computing; Rough set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing (GrC), 2013 IEEE International Conference on
Conference_Location :
Beijing
Type :
conf
DOI :
10.1109/GrC.2013.6740418
Filename :
6740418
Link To Document :
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