From Big Data Analytics To Smart Data Analytics With Parallelization Techniques

The massively increasing amount of often geographically dispersed large quantities of data of experiments, observations, or computational simulations become ever more important for science, research, industry and governments. Scientists and engineers that analyse these massive datasets require therefore reliable infrastructures as well as scalable tools in order to perform ‘scientific big data analytics (SBDA)’ using parallelization techniques. This talk will provide insights what infrastructure types are available in order to take advantage of such parallel methods, including high performance computing, high throughput computing, and cloud computing approaches and capabilities. It will survey selected parallel tools that enable a scalable data analysis and realistic computational simulations also motivating the current trend towards hybrid modelling and scientific and engineering use cases in which large-scale computing gets more intertwined with traditional data analysis