期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：6
页码：12757-12765
出版社：IJECS
摘要：With the advent of service computing and cloud computing, more and more services are emerging on the Internet, generatinghuge volume of data. The overwhelming service-generated data become too large and complex to be effectively processed by traditionalapproaches. How to store, manage, and create values from the service-oriented big data become an important research problem. On theother hand, with the increasingly large amount of data, a single infrastructure which provides common functionality for managing andanalyzing different types of service-generated big data is urgently required.Nowadays, users are accessing multiple data storage platforms to accomplish their operational and analytical requirements. Efficientintegration of different data sources is important. For example, an organization may purchase storage from different vendors and need tocombine data with different format stored on systems from different vendors. Data integration, which plays an important role for bothcommercial and scientific domains, combines data from different sources and provides users with a unified view of these data. How tomake efficient data integration with the 4V (volume, velocity, variety, and veracity) characteristics is a key research direction for the bigdata platforms.To address this challenge, this paper describes how cloud and big data technologies are converging to offer a cost-effective delivery model forcloud-based big data analytics. It also includes how cloud computing is an enabler for advanced analytics with big data and how IT canassume leadership for cloud-based big data analytics in the enterprise by becoming a broker of cloud services
