“Deep storage, and tape library-based storage in general, benefit organizations that are looking to incorporate low-cost, high-density, scalable storage into their fast-growth data environments. Industries that recognize the value and regularly rely on tape storage include education, federal and state government, finance, life sciences, media and entertainment, oil and gas exploration, and Web 2.0, among others.”
We sat down with Cristian Borcea, PhD from the New Jersey Institute of Technology to discuss the IoT and Big Data applications. “New machine learning techniques could help us extract knowledge from these data – this happens especially for knowledge that we don’t expect and we don’t even know exists – we cannot search for something that we don’t know exists.”
We caught up with Mike Boros, Hadoop Product Manager at Cray, to learn about the company’s Big Data solutions. “I think you’ll see Cray continue to focus on Big & Fast, vs. just Big Data. Technologies like Hadoop make hosting large data sets easy. The challenge of getting value from that data set, after it’s large, is what we’re interested in.”
DK Panda from Ohio State University presented this talk at the Stanford HPC & Exascale Conference. “As InfiniBand is getting used in scientific computing environments, there is a big demand to harness its benefits for enterprise environments for handling big data and analytics. This talk will focus on high-performance and scalable designs of Hadoop using native RDMA support of InfiniBand and RoCE.”
“Map-D uses multiple NVIDIA GPUs to interactively query and visualize big data in real-time. Map-D is an SQL-enabled column store that generates 70-400X speedups over other in-memory databases. This talk discusses the basic architecture of the system, the advantages and challenges of running queries on the GPU, and the implications of interactive and real-time big data analysis in the social sciences and beyond.”
In this talk, Sean Gourley examines this world of augmented intelligence and shows how our understanding of the human brain is shaping the way we visualize and interact with big data. Gourley argues that the world we are living in is too complex for any single human mind to understand and that we need to team up with machines to make better decisions.
In this video, Gregory Piatetsky-Shapiro from KDNuggets and Michael Karasick from IBM discuss the current state of Big Data analytics and where it might be intersecting with cognitive computing in the future.
Big Data continues to touch an increasing number of segments of business and science so it should be no surprise that climate science researchers are now using these technologies to gain insight into anthropogenic sources of climate change.
In a significant coupling of scientific research and big data, NASA and Amazon Web Services Inc. (AWS) are making a large collection of NASA climate and Earth science satellite data available to research and educational users through the AWS cloud.
Challenge to be first in a series to crowdsource Alzheimer’s data for new disease insights .