Title :
Towards Real-Time Analytics in the Cloud
Author :
Osman, Ahmed ; El-Refaey, Mohamed ; ElNaggar, Ayman
Author_Institution :
Comput. Sci. Dept., German Univ. in Cairo, Cairo, Egypt
fDate :
June 28 2013-July 3 2013
Abstract :
The data explosion and the tremendous growth in the volume of data generated from various IT services places an enormous demand on harnessing and smartly analyzing the generated data and enterprise contents. According to recent studies, it is predicted that the volume of such data will become 26 fold in the next five years. While there might be some existing technologies to support this, industry is frantically exploring new models that lead to more efficient and higher performance solutions. With the aid of cloud computing and high performance analytics such as scalable-parallel machine learning, big data could be the fuel to a smarter cloud-powered IT world. Through our work, we provide a state-of-the-art review of high-performance advanced cloud analytics in the literature in attempt to find the ideal real-time platform for distributed analytic computations.
Keywords :
cloud computing; data analysis; learning (artificial intelligence); parallel programming; cloud computing; data analysis; data explosion; data volume; distributed analytic computations; high performance analytics; information technology; realtime analytics; scalable-parallel machine learning; smarter cloud-powered IT world; Data handling; Data mining; Data storage systems; Distributed databases; Information management; Optimization; Real-time systems; Cloud Computing; Real-time cloud analytics; Stream Computing; Map-reduce; Distributed Machine learning; Hadoop; Big data;
Conference_Titel :
Services (SERVICES), 2013 IEEE Ninth World Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5024-4
DOI :
10.1109/SERVICES.2013.36
