Parallel Stream Processing with MPI for Video Analytics and Data Visualization The amount of data generated is increasing exponentially. However, processing data and producing fast results is a technological challenge. Parallel stream processing can be implemented for handling high frequency and big data flows. The MPI parallel programming model offers lowlevel and flexible mechanisms for dealing with distributed architectures such as clusters. This paper aims to use it to accelerate video analytics and data visualization applications so that insight can be obtained as soon as the data arrives. Experiments were conducted with a DomainSpecific Language for Geospatial Data Visualization and a Person Recognizer video application. We applied the same stream parallelism strategy and two task distribution strategies. The dynamic task distribution achieved better performance than the static distribution in the HPC cluster. The data visualization achieved lower throughput with respect to the video analytics due to the IO intensive operations. Also, the MPI programming model shows promising performance outcomes for stream processing applications.

A Change Detection Approach for Processing Outdoor Scenes on Hadoop Clusters Processing video data is becoming more useful in a wide range of applications. However, video data are demanding for computing resources, such as processor, memory, and disk. This is because the data size is huge in nature and growing exponentially. Change detection is a commonly used method in a variety of video processing applications, so it has been attracting the attention of many researchers. The goal of improving the speed of change detection could be to satisfy realtime performance or to process larger data in a timely manner. This study proposes an approach based on MapReduce and sampling to improve the performance of using change detection to process large video data on Hadoop clusters. The experiments, conducted on an outdoor scene dataset, show significant improvement in the execution time.