Enabling actionable analytics for mobile devices: performance issues of distributed analytics on Hadoop mobile clusters
Seungbae Lee, Kanika Grover, Alvin Lim · Journal of Cloud Computing Advances Systems and Applications · 2013
Significant innovations in mobile technologies are enabling mobile users to make real-time actionable decisions based on balancing opportunities and risks to take coordinated actions with other users in their workplace. This requires a new distributed analytic framework that collects relevant information from internal and external sources, performs real-time distributed analytics, and delivers a critical analysis to any user at any place in a given time frame through the use of mobile devices such as smartphones and tablets. This paper discusses the advantages and challenges of utilizing mobile devices for distributed analytics by showing its feasibility with Hadoop analytic framework.