Presentation on theme: "Workshop Organizing Committee: Rosalind R. JamesCarolyn Lawrence Sharon PapiernikCurt Van Tassell."— Presentation transcript:
Workshop Organizing Committee: Rosalind R. JamesCarolyn Lawrence Sharon PapiernikCurt Van Tassell
Workshop Purpose Bring ARS scientific capability to the cutting edge
Workshop Purpose Develop a vision and strategy that defines: (1)ARS scientific Big Data needs (2)An infrastructure for dealing with these needs for now and into the future
What is Big Data? Massive amounts of data that collect over time that are difficult to analyze and handle using common data management tools.
Big Data comes in V-Dimensions: Volume. With large size comes difficulty in finding what is relevant, space to store it, and how to index it Variety. Highly structured data, variability structured data, and unstructured data Velocity. How fast is the data created, and how fast must it be processed? Veracity. Uncertain or imprecise data.
What makes Big Data so important? Researchers no longer simply ask, What experimental design will best address this question? But rather, What can I glean from extant data? Or better yet, What insights can I glean if I could fuse data from multiple domains? From: The Fourth Paradigm: Data-Intensive Scientific Discovery
EO Wilson. 1998. Consilience, The Utility of Knowledge
Scientific computing is becoming increasingly data intensive. We are becoming increasingly able to Answer previously intractable questions, More efficiently solve problems, Characterize the natural world to a greater level of detail
An era of large datasets Large Hadron Collider 15 Pbytes/year (15 x 10 6 Gbytes, 15 x 10 3 Tbytes) Pan-STARRS (panoramic survey telescope) 2Gbytes per image, taken every 30 sec from 4 cameras Several Tbytes/night/telescope Natl. Human Genome Research Institute 1000 genomes = 200 Tbytes Beijing Genomics Institute 5 Tbytes/day
What it takes to move Big Data 1Gbyte data T1 line: 1.5 hrs Thin Ethernet: 14 min Fast Ethernet: 1 min 1 Tbyte data T1 line: 65 days, 22.5 hrs Thin ethernet: 10 days, 4.3 hrs Fast ethernet: 1 day, 0.5 hrs Gig-E: 2 hrs, 26 min.
Moving into the cloud Scientists need to be able to move and share large datasets. Cloud/Cluster/Grid computing. Not just for holding data, but for computations Reduce the need to repeatedly move the same datasets.
Libraries: Provide access and dissemination of information…
Existing Systems for Handling Big Data XCEDE (replaces TeraGrid) A virtual system that scientists can use to interactively share super computer resources, data, & expertise Composite of several university advanced computer centers iPlant (Texas Advanced Computing Center) Plant genomic data Cyber infrastructure for the transfer, storage, analysis, visualization, meta-data control, discovery, etc. Cloud computing
Existing Big Data Systems (cont.) Three Rivers Optical Exchange (part of XCEDE) Amazon Cloud Computing Purchase computing power and storage, as needed John Wesley Powell Center for Analysis & Synthesis USGS Earth sciences issues Enhancing scientific discovery & problem solving through integrated research. European grid systems Watson (?)
ARS Could Provide Leadership for Agricultural Data OSTP Big Data Research and Development Initiative John Holdren (3/29/2012) The government is under investing in data management The process of going from data knowledge understanding is being inhibited Human capital needs People with deep analytical skills, Data-savvy managers/executives Greater IT savvy technicians, for both structured and unstructured data
What does ARS have to add? Decision support software operate from a cloud system Public databases could be better organized and more easily accessible, collectively Large data Currently wasting money on redundant hardware And software Currently have difficulty moving the data Cloud systems facilitate fusing datasets ARS capable of long-term stability for storage, analyses
Thus this Workshop Will Gather together ARS scientists who are already working with large data or with experience and knowledge of our current database collections or who are trying to work with Big Data Include speakers familiar with Big Scientific Data issues, who have developed solutions Develop a Vision for what an ARS solution should look like.
A white paper describing a vision for ARS Big Data, including examples of current needs and an infrastructure for meeting current and future needs. This infrastructure will include IT resources Intellectual resources Personnel resources
ARS Administrators (AC Council) ARS Office National Programs OCIO and IT Specialists in the Field ARS Scientific Staff (scientists, technicians, computational biologists, statisticians)
The climb is steep, but there are cairns along the way.