The era of Big Data has arrived. The paradigm shift towards “Data Intensive Science” requires a reexamination of the infrastructure and capabilities required by researchers, and even of the scientific process itself.
Big data implies not only large data volumes but also discovering complex relationships among different data sets that will require innovative capabilities to aggregate and analyze distributed data. With the large amounts of data streaming in from observational sensors or other sensing devices, as well as output generated by model simulations, data also needs to be viewed as a resource asset rather than a constraint.
In fact, business organizations have started viewing “data as the new oil” with the enormous potential to extract information and knowledge. However, just as oil, data can be extremely messy and requires specialized skills to filter, transform or refine into something useful.
This session has some presentations from interested participants and seeks other presentations focused on challenges and technological solutions for exploiting big data for research and applications. Some of the suggested topics include:
- • Data Mining
- • Interactive data exploration and search
- • Data access
- • Visual Analytics
- • New tools and applications
- • Infrastructure
Session Leads: Dr. Sara Graves (firstname.lastname@example.org)
Dr. Rahul Ramachandran (email@example.com)
An example of a possible presentation for this session is the abstract entitled:
Inter-‐comparison of Big Data Technologies for Analysis of Earth Science Data Kwo-Sen Kuo1, Thomas L. Clune2, Daniel Q. Duffy3, Gyorgy Fekete4, Rahul Ramachandran5, John A. Rushing6 (alphabetical order after the first author)