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Compared to the traditional data storing, processing, analyzing and visualization which have been performed, Big data requires evolutionary technologies of massive data processing on distributed and parallel systems, such as Hadoop system. Big data analytic systems, thus, have been popular to derive important decision making in various areas. However, visualization on analytic system faces various limitation due to the huge amount of data. This brings the necessity of interactive visualization techniques beyond the traditional static visualization. R has been used and improved for a big data analysis and mining tool. Also, R is supported with various and abundant packages for different targets with visualization. However interactive visualization packages are not easily found in the market. This paper compares and analyzes interactive web packages with visualization packages for R. This paper also proposes interactive web visualized analysis environment for big data with a combination of interactive web packages and visualization packages. In particular, Big data analysis techniques with sensed data are presented as the result by reflecting the decision view on sensing field.

With the increasing complexity of recent autonomous platforms, there is a strong demand to better utilize system resources while satisfying stringent real-time requirements. Embedded virtualization is an appealing technology to meet this demand. It enables the consolidation of real-time systems with different criticality levels on a single hardware platform by enforcing temporal isolation. On multi-core platforms, however, shared hardware resources, such as caches and memory buses, weaken this isolation. In particular, due to the resulting cache interference, a large last-level cache in recent processors can easily jeopardize the timing predictability of real-time tasks due to cache interference. While researchers in the real-time systems community have developed solutions to tackle this problem, existing cache management schemes reveal two major limitations when used in a clustered multi-core embedded system. The first is the cache co-partitioning problem, which can lead to wrong cache allocation and cache underutilization. The second is the cache interference of inter-virtual-machine (VM) communication because prior work has considered only independent tasks. This paper presents a cluster-aware real-time cache allocation scheme to address these problems. The proposed scheme takes into account the cluster information of the system, and finds the cache allocation that satisfies the timing and memory requirements of tasks. The scheme also maximizes slack time to meet task deadline, which brings flexibility and resilience to unexpected events. Tasks using inter-VM communication are also provided with guaranteed blocking time and cache isolation. We have implemented a prototype of our scheme on an Nvidia TX2 clustered multi-core platform and evaluated the effectiveness of our scheme over cluster-unaware approaches. INDEX TERMS Cache interference, clustered multi-core platforms, real-time systems, embedded virtualization, real-time hypervisor, partitioning hypervisor, real-time resource management.

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Abstract. Beyond the hybrid cloud computing and its integration service, Mobile Cloud Computing (MCC) starts to cultivate personal cloud computing areas with enormous services and applications with rapid increase of personal use of mobile devices. This paper presents the motivation of MCC with unique features which are discriminated from those of traditional cloud computing, and also shows the architectural design strategies with recent technologies to realize the MCC. We organize the taxonomy of architectural design strategy under five focuses: data-intensive architectural design, mobile processing-oriented design, service-aware design, privacy-sensitive design, and analytical-purpose design. We also outline the design metrics to archive a smart MCC.