This survey article reviews the challenges associated with deploying and optimizing big data
applications and machine learning algorithms in cloud data centers and networks. The …

Cloud computing has emerged as an important paradigm to improve resource utilization,
efficiency, flexibility, and the pay-per-use billing structure. However, cloud platforms cause …

This paper proposes a conceptual model for which one might begin to assess aesthetic
effect within the user experience of information visualization. Through first defining, then …

One of the key challenges in big data technology is the velocity at which the data is
processed. Hadoop, an open-source software framework, is the dominant technology to …

Memes influence the behaviours, perceptions and actions of the people they spread to, both
consciously and unconsciously. The production of memes and memetic behaviours are …

Big data phenomenon is a concept for large, heterogeneous and complex data sets and
having many challenges in storing, preparing, analyzing and visualizing as well as …

Distributed data processing systems like MapReduce, Spark, and Flink are popular tools for
analysis of large datasets with cluster resources. Yet, users often overprovision resources for …

In recent years, the widespread use of container technologies has significantly altered the
interactions between cloud service providers and their customers when developing and …

Apache Hadoop is a famous framework for big data science. Most of the research for
improving the speed of big data analysis is researching based on Hadoop modules such as …

In order to support emerging data-intensive applications, many clever frameworks have
been developed over the last years to efficiently and distributedly process big data sets …