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Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text categorization can lead to time delays in topic detection. In this paper we construct similar...
Video data has become the largest source of big data. Owing to video data's complexities, velocity, and volume, public security and other surveillance applications require efficient, intelligent runtime video processing. To address these challenges, a proposed framework combines two cloud-computing technologies: Storm stream processing and Hadoop batch processing. It uses deep learning to realize...
Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not...
Big data analytics (BDA) applications are software applications that process huge amounts of data using large-scale parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open source BDA processing framework, which implements the MapReduce programming paradigm. In many cases, BDA jobs are continuous and not mutually separated. Existing work on processing jobs in sequence...
Big Data processing has become the common business needs in government and enterprise applications, e.g., Analysis or detection of climate change, economic development, or online customer behavior. Hadoop is the most mature open source big data processing framework, which implements the MapReduce programming paradigm. The mass source data are stored in HDFS supported by Hadoop and processed parallelly...
Building big data analytics (BDA) applications in the cloud introduces inevitable challenges, such as loss of control and uncertainty. To address the existing challenges, numerous efforts have been made on BDA application engineering to optimize the quality of BDA applications in the cloud, such as performance and reliability. However, there is still a lack of systematic view on engineering BDA applications...
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