Core Techniques and Technologies for Advancing Big Data Science & Engineering (BIGDATA) NSF
|
|
|
- Britney Lee
- 10 years ago
- Views:
Transcription
1 Core Techniques and Technologies for Advancing Big Data Science & Engineering (BIGDATA) NSF Vasant Honavar Program Director Information & Intelligent Systems (IIS) Division Computer and Information Science and Engineering (CISE) Directorate National Science Foundation 1
2 Big Data Research and Development Initiative Big Data Senior Steering Group chartered in spring 2011 under the Networking and Information Technology R&D (NITRD) Program Members from DARPA, DOD OSD, DHS, DOE-Science, HHS, NARA, NASA, NIST, NOAA, NSA, and USGS Co-chaired by NIH and NSF White House Big Data Launch March 29, 2012 Long-term, National Big Data R&D with four major components: Foundational research to develop new techniques and technologies to derive knowledge from data New cyberinfrastructure to manage, curate, and serve data to research communities New approaches for education and workforce development Challenges and competitions to create new data analytic ideas, approaches, and tools from a more diverse stakeholder population 2
3 The Big Data Team Suzi Iacono, NSF, Karin Remington, NIH NITRD Big Data Steering Group Vasant G. Honavar, NSF - CISE Jia Li, NSF - MPS Dane Skow, NSF OCI Peter H. McCartney, NSF - BIO Doris L. Carver, NSF - EHR Eduardo A. Misawa, NSF - ENG Eva Zanzerkia, NSF - GEO Peter Muhlberger, NSF - SBE Vladimir Papitashvili, NSF OPP Peter Lyster, NIH - NIGMS Karin A. Remington, NIH - NIGMS Jerry Li, NIH - NCI Vinay M. Pai, NIH - NIBIB Karen Skinner, NIH - NIDA Yuan Liu, NIH - NINDS Valerie Florance, NIH - NLM Vivien Bonazzi, NIH - NHGRI Fen Zhao, AAAS, NSF - CISE 3
4 Outline Welcome Suzi Iacono and Karin Remington Webinar Vasant Honavar Background Data Deluge Research Opportunities and Challenges BIGDATA Program in Context BIGDATA Solicitation Scope Research Thrusts Proposal Types and Deadlines Proposal preparation and submission Proposal Review Process Q & A Please your questions to [email protected] 4
5 9000 Data Deluge Quantity of Global Digital Data, Exabytes Source: EMC/IDC Digital Universe Study,
6 Dealing with Data Big data is not just about volume or rate of acquisition, but also Heterogeneity/diversity Multiple levels of granularity Multiple media and modalities Scientific disciplines Complexity Uncertainty Incompleteness Representation 6
7 Scientific Data Data Deluge Digital Media VIDEO BLOGS MOBILE VOIP IM Human Sensors Health Care Evaluate Sense Public Intervene Identify Personal Social Assess Source: Sajal Das, Keith Marzullo 7
8 Opportunities Big Data presents unprecedented opportunities to Accelerate scientific discovery and innovation Lead to new fields of inquiry that would not otherwise be possible Improve decision making Understand human and social processes Promote economic growth Improve health and quality of life 8
9 Dealing with Data Picture of The Economist magazine pending Image permission approval Picture of The Fourth Paradigm Pending Image Permission approval 9
10 Examples of Research Challenges More data is being collected than we can store Analyze the data as it becomes available Decide what to archive and what to discard Many data sets are too large to download Analyze the data wherever it resides Many data sets are too poorly organized to be usable Better organize and retrieve data Many data sets are heterogeneous in type, structure, semantics, organization, granularity, accessibility Integrate and customize access to federated data Utility of data limited by our ability to interpret and use it Extract and visualize actionable knowledge Evaluate results 10
11 BIG DATA Initiative in Context: NSF Cyber-infrastructure for 21 st Century (CIF21) Vision Expertise Organizations Scientific Instruments Computational Resources Discovery, Discovery Innovation, Collaboration Education Education Networking Data and Knowledge BIG DATA Software
12 BIGDATA Solicitation in Context BIGDATA solicitation is one component of a national big data initiative Focus: research on core techniques and technologies Additional BIGDATA opportunities Computational and Data-enabled Science and Engineering (NSF) CDS&E-MSS: CDS&E-ENG: Big data infrastructure projects (NSF) DIBBs: Education and workforce development efforts (NSF) IGERT-CIF21: Complex multi-disciplinary grand challenge problems Prizes and competitions Other Related Opportunities NSF Core Program Solicitations: CISE/IIS: BISTI: Biomedical Information Science and Technology Initiative (NIH) Additional solicitations and dear colleague letters (NSF): 12
13 BIGDATA Solicitation BIGDATA seeks proposals that develop and evaluate core techniques and tools within three thrust areas: Data management, collection and storage (DCM) Data analytics (DA) E-science collaboration environments (ESCE) 13
14 Data management, collection and storage (DCM) Potential DCM research areas include, but are not limited to: New data storage, I/O, architectures Efficient archiving, storing, indexing, retrieving, and recovery Streaming, filtering, compressed sensing, sufficient statistics Automatic data annotation Data discovery, workflows, provenance Advanced data architectures Data validity, integrity, consistency, uncertainty management Languages, tools, methodologies and programming environments 14
15 Data Analytics (DA) Potential DA research areas include, but are not limited to: Scalable Machine learning, statistical inference, and data mining Predictive modeling, hypothesis generation and automated discovery New algorithms, programming languages, data structures for data analytics Data-driven high fidelity simulations Information extraction from unstructured, multimodal data Scalable and interactive data visualization Extraction and integration of knowledge from massive, complex, multi-modal, or dynamic data Data analytics under processing, memory, storage, access, energy, constraints 15
16 E-science collaboration environments (ESCE) Potential DA research areas include, but are not limited to: Automated and Interactive Discovery Processes Scientific Workflows Novel collaboration tools Data, knowledge, and model sharing Remote operation, scheduling, and real-time remote access to instruments and data resources 16
17 NIH BIGDATA Priorities BIGDATA core technologies and tools for Self-sustaining automated approaches to archiving, mining, retrieving, and analyzing diverse biomedical or behavioral research data Analysis of structural and functional connectomes Analysis of social media for understanding local, regional, national and global health Mapping the current state of biomedical research landscape Collaborative, integrative analyses of disparate data from multiple clinical research projects and clinical trials Predictive modeling of primary biological and pathological driving factors and processes underlying disease and treatment response Analysis of large volume of patient data for real-time individualized and optimal diagnosis and treatment plans In silico science to generate or test hypotheses Interactive publications that provide access to data and enable data reuse and reanalysis 17
18 National Priorities Proposals may, but are not required to, focus on core techniques and technologies needed in areas of national priority Advanced Manufacturing Health IT Emergency response and preparedness Clean energy Cyberlearning Material genome National security 18
19 What proposals are good fits for the BIGDATA solicitation? The focus of this solicitation is on core scientific and technological advances (e.g., in computer and information sciences and engineering, mathematics, or statistics) needed to take advantage of available data sets to accelerate discovery Proposals may address research challenges within one or more of the three thrusts: Data management, collection and storage (DCM) Data analytics (DA) E-science collaboration environments (ESCE) 19
20 What proposals are not good fits for the BIGDATA Solicitation? Proposals that focus primarily on Implementing existing techniques or technologies Applying existing techniques (e.g., machine learning, statistical analyses) to specific data sets Developing databases to serve specific scientific communities using existing database technologies 20
21 Proposal Submission and Review All proposals shall be submitted to NSF All proposals are reviewed by panels according to Standard NSF merit review criteria and Solicitation-specific review criteria Data Management and Software Sharing Plan Capacity building plan Evaluation plan Coordination plan (required for mid-scale proposals) Panels will include panelists from the NSF and NIH PI communities as appropriate PIs may target a specific agency sponsorship only if they have Communicated with a program officer from that agency; and Received permission or instruction to do so Proposals that are not targeted to a specific agency will be considered for funding by NSF or NIH 21
22 Review Criterion: Intellectual Merit Intellectual Merit (encompasses all of the following) How important is the proposed activity to advancing knowledge and understanding within its own field or across different fields? How well qualified is the proposer to conduct the project? To what extent does the proposed activity suggest and explore creative, original, or potentially transformative concepts? How well conceived and organized is the proposed activity? Is there sufficient access to resources? 22
23 Review Criterion: Intellectual Merit Intellectual Merit (encompasses all of the following) significance investigator(s) innovation approach environment 23
24 Review Criterion Capacity Building (CB) Each BIGDATA proposal must include a description of at least one capacity building (broader impacts) activity Examples of CB activities: Education and outreach Broadening participation Cost models Data management models Community data standards Access policies Economic sustainability models Communication strategies See 24
25 Evaluation Plan Each proposal must include a plan to evaluate the techniques and technologies developed through for example, Applications of the technology to specific domains Assessments of effectiveness The evaluation plan should be appropriate for: The nature of the research activities involved The size and scope of the project 25
26 Data Sharing Plan The types of data, software, curriculum materials, and other materials to be produced in the course of the project Standards to be used for data and metadata format and content Policies for access and sharing Policies and provisions for re-use, re-distribution, and the production of derivatives Plans for archiving and for preservation of access 26
27 Software Sharing Plan The software should be freely available to researchers and educators in the non-profit sector The terms of availability should permit The dissemination and commercialization of enhanced or customized versions of the software, or its incorporation into other software packages Further development by other groups Modification to the source code and sharing of modifications An applicant Is responsible for creating the original and subsequent official versions of software May consider proposing a plan to manage and disseminate the improvements or customizations of their tools and resources by others 27
28 Mid-scale proposals: Coordination plan (CP) Must include The specific roles of the collaborating PIs, Co-PIs, other Senior Personnel and paid consultants at all organizations involved How the project will be managed across institutions and disciplines Specific coordination mechanisms for cross-institution and/or crossdiscipline scientific integration e.g., workshops, graduate student exchange, project meetings, videoconferencing and other communication tools, software repositories Specific references to the budget line items that support coordination Coordination plan may use two additional pages (beyond the 15 pages) in the Project Description Mid scale proposals that do not include a coordination plan will be returned without review 28
29 Proposal Types and Deadlines NSF: Mid-scale projects: Due June 13, 2012 (5 p.m. proposer's local time): Typically three or more investigators Budgets up to $1000,000 (total) per year for up to 5 years Small projects: Due July 11, 2012 (5 p.m. proposer's local time) Typically one or two investigators Budgets up to $250,000 (total) per year for up to 3 years Note: NIH has comparable budget limits NIH: 29
30 How many awards are anticipated? Up to $25 million will be invested in proposals submitted in response to this solicitation, subject to availability of funds, during An estimated fifteen to twenty projects will be funded by NSF and/or NIH during FY 2012 and FY 2013 subject to availability of funds. See html for NIH-Specific guidance. 30
31 How does one apply? Follow the instructions provided in: The solicitation The Proposal and Award Policies and Procedures Guide NIH html Consult FastLane FAQ and Grants.gov FAQ: Your institution s Sponsored Research Office 31
32 Questions and Answers We will answer selected questions sent through Answers to all questions will be included in the FAQ Further Questions? BIGDATA FAQ: 32
33 Credits Copyrighted material used under Fair Use. If you are the copyright holder and believe your material has been used unfairly, or if you have any suggestions, feedback etc., please contact: Except where otherwise indicated, permission is granted to copy, distribute, and/or modify all images in this document under the terms of the GNU Free Documentation license, Version 1.2 or later published by the Free Software Foundation; with no Invariant Sections, no Front- Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled GNU Free Documentation license at: ation_license The inclusion of a logo does not express or imply the endorsement by NSF of the entities' products, services or enterprises 33
Big Data R&D Initiative
Big Data R&D Initiative Howard Wactlar CISE Directorate National Science Foundation NIST Big Data Meeting June, 2012 Image Credit: Exploratorium. The Landscape: Smart Sensing, Reasoning and Decision Environment
CYBERINFRASTRUCTURE FRAMEWORK FOR 21 ST CENTURY SCIENCE, ENGINEERING, AND EDUCATION (CIF21)
CYBERINFRASTRUCTURE FRAMEWORK FOR 21 ST CENTURY SCIENCE, ENGINEERING, AND EDUCATION (CIF21) Overview The Cyberinfrastructure Framework for 21 st Century Science, Engineering, and Education (CIF21) investment
National Big Data R&D Initiative
National Big Data R&D Initiative Suzi Iacono, PhD National Science Foundation Co-chair NITRD Big Data Senior Steering Group for CASC Spring Meeting April 23, 2014 Why is Big Data Important? Transformative
CYBERINFRASTRUCTURE FRAMEWORK FOR 21 ST CENTURY SCIENCE, ENGINEERING, AND EDUCATION (CIF21) $100,070,000 -$32,350,000 / -24.43%
CYBERINFRASTRUCTURE FRAMEWORK FOR 21 ST CENTURY SCIENCE, ENGINEERING, AND EDUCATION (CIF21) $100,070,000 -$32,350,000 / -24.43% Overview The Cyberinfrastructure Framework for 21 st Century Science, Engineering,
CYBERINFRASTRUCTURE FRAMEWORK FOR 21 st CENTURY SCIENCE AND ENGINEERING (CIF21)
CYBERINFRASTRUCTURE FRAMEWORK FOR 21 st CENTURY SCIENCE AND ENGINEERING (CIF21) Goal Develop and deploy comprehensive, integrated, sustainable, and secure cyberinfrastructure (CI) to accelerate research
SECURE AND TRUSTWORTHY CYBERSPACE (SaTC)
SECURE AND TRUSTWORTHY CYBERSPACE (SaTC) Overview The Secure and Trustworthy Cyberspace (SaTC) investment is aimed at building a cybersecure society and providing a strong competitive edge in the Nation
Big Data R&D Initiative
Big Data R&D Initiative Mhyron Gutmann Directorate for the Social, Behavioral and Economic Sciences National Science Foundation Image Credit: Exploratorium. Digital Preservation 2012 July 25, 2012 Advances
NITRD and Big Data. George O. Strawn NITRD
NITRD and Big Data George O. Strawn NITRD Caveat auditor The opinions expressed in this talk are those of the speaker, not the U.S. government Outline What is Big Data? Who is NITRD? NITRD's Big Data Research
Big Data. George O. Strawn NITRD
Big Data George O. Strawn NITRD Caveat auditor The opinions expressed in this talk are those of the speaker, not the U.S. government Outline What is Big Data? NITRD's Big Data Research Initiative Big Data
NASA Earth Science Research in Data and Computational Science Technologies Report of the ESTO/AIST Big Data Study Roadmap Team September 2015
NASA Earth Science Research in Data and Computational Science Technologies Report of the ESTO/AIST Big Data Study Roadmap Team September 2015 I. Background Over the next decade, the dramatic growth of
Data Science at the NIH Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health
Data Science at the NIH Philip E. Bourne Ph.D. Associate Director for Data Science National Institutes of Health Data Science Timeline 6/12 Findings: Sharing data & software through catalogs Support methods
How To Teach Data Science
The Past, Present, and Future of Data Science Education Kirk Borne @KirkDBorne http://kirkborne.net George Mason University School of Physics, Astronomy, & Computational Sciences Outline Research and Application
Computational Science and Informatics (Data Science) Programs at GMU
Computational Science and Informatics (Data Science) Programs at GMU Kirk Borne George Mason University School of Physics, Astronomy, & Computational Sciences http://spacs.gmu.edu/ Outline Graduate Program
NITRD: National Big Data Strategic Plan. Summary of Request for Information Responses
NITRD: National Big Data Strategic Plan Summary of Request for Information Responses Introduction: Demographics Summary of Responses Next generation Capabilities Data to Knowledge to Action Access to Big
The National Consortium for Data Science (NCDS)
The National Consortium for Data Science (NCDS) A Public-Private Partnership to Advance Data Science Ashok Krishnamurthy PhD Deputy Director, RENCI University of North Carolina, Chapel Hill What is NCDS?
Data Driven Discovery In the Social, Behavioral, and Economic Sciences
Data Driven Discovery In the Social, Behavioral, and Economic Sciences Simon Appleford, Marshall Scott Poole, Kevin Franklin, Peter Bajcsy, Alan B. Craig, Institute for Computing in the Humanities, Arts,
Big Data to Knowledge (BD2K)
Big Data to Knowledge () potential funding agency synergies Jennie Larkin, PhD Office of the Associate Director of Data Science National Institutes of Health idash-pscanner meeting UCSD September 16, 2014
High Performance Computing Initiatives
High Performance Computing Initiatives Eric Stahlberg September 1, 2015 DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health National Cancer Institute Frederick National Laboratory is
SECURE AND TRUSTWORTHY CYBERSPACE (SaTC) $124,250,000 +$1,500,000 / 1.2%
SECURE AND TRUSTWORTHY CYBERSPACE (SaTC) $124,250,000 +$1,500,000 / 1.2% Overview The Secure and Trustworthy Cyberspace (SaTC) investment is aimed at building a cybersecure society and providing a strong
Last Modified: August 25, 2015. Table of Contents. Page 1 of 15
National Science Foundation Office of International Science and Engineering How to Apply to the East Asia and Pacific Summer Institutes (EAPSI) Fellowship Program for U.S. Graduate Students via NSF FastLane
Databases & Data Infrastructure. Kerstin Lehnert
+ Databases & Data Infrastructure Kerstin Lehnert + Access to Data is Needed 2 to allow verification of research results to allow re-use of data + The road to reuse is perilous (1) 3 Accessibility Discovery,
Data Management Considerations for the Data Life Cycle
Data Management Considerations for the Data Life Cycle NRC STS Panel 2011 November 17, 2011, Washington DC Peter Fox (RPI) [email protected], [email protected] Tetherless World Constellation http://tw.rpi.edu
NIH Commons Overview, Framework & Pilots - Version 1. The NIH Commons
The NIH Commons Summary The Commons is a shared virtual space where scientists can work with the digital objects of biomedical research, i.e. it is a system that will allow investigators to find, manage,
EL Program: Smart Manufacturing Systems Design and Analysis
EL Program: Smart Manufacturing Systems Design and Analysis Program Manager: Dr. Sudarsan Rachuri Associate Program Manager: K C Morris Strategic Goal: Smart Manufacturing, Construction, and Cyber-Physical
Survey of Canadian and International Data Management Initiatives. By Diego Argáez and Kathleen Shearer
Survey of Canadian and International Data Management Initiatives By Diego Argáez and Kathleen Shearer on behalf of the CARL Data Management Working Group (Working paper) April 28, 2008 Introduction Today,
COGNITIVE SCIENCE AND NEUROSCIENCE
COGNITIVE SCIENCE AND NEUROSCIENCE Overview Cognitive Science and Neuroscience is a multi-year effort that includes NSF s participation in the Administration s Brain Research through Advancing Innovative
Manjula Ambur NASA Langley Research Center April 2014
Manjula Ambur NASA Langley Research Center April 2014 Outline What is Big Data Vision and Roadmap Key Capabilities Impetus for Watson Technologies Content Analytics Use Potential use cases What is Big
Summary of Responses to the Request for Information (RFI): Input on Development of a NIH Data Catalog (NOT-HG-13-011)
Summary of Responses to the Request for Information (RFI): Input on Development of a NIH Data Catalog (NOT-HG-13-011) Key Dates Release Date: June 6, 2013 Response Date: June 25, 2013 Purpose This Request
Government Technology Trends to Watch in 2014: Big Data
Government Technology Trends to Watch in 2014: Big Data OVERVIEW The federal government manages a wide variety of civilian, defense and intelligence programs and services, which both produce and require
Funding Opportunities at The National Science Foundation (NSF) Related to Education at Scale. Janet Kolodner CISE/IIS and EHR/DRL
Funding Opportunities at The National Science Foundation (NSF) Related to Education at Scale Janet Kolodner CISE/IIS and EHR/DRL Who Are We? The National Science Foundation is an independent federal agency
IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper
IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper CAST-2015 provides an opportunity for researchers, academicians, scientists and
A Hurwitz white paper. Inventing the Future. Judith Hurwitz President and CEO. Sponsored by Hitachi
Judith Hurwitz President and CEO Sponsored by Hitachi Introduction Only a few years ago, the greatest concern for businesses was being able to link traditional IT with the requirements of business units.
Division of Behavioral and Cognitive Sciences STRATEGIC PLAN
Division of Behavioral and Cognitive Sciences STRATEGIC PLAN NATIONAL SCIENCE FOUNDATION Directorate for Social, Behavioral, and Economic Sciences November 2011 National Science Foundation Directorate
MEDICAL DATA MINING. Timothy Hays, PhD. Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012
MEDICAL DATA MINING Timothy Hays, PhD Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012 2 Healthcare in America Is a VERY Large Domain with Enormous Opportunities for Data
3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India
3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India Call for Papers Cloud computing has emerged as a de facto computing
Conquering the Astronomical Data Flood through Machine
Conquering the Astronomical Data Flood through Machine Learning and Citizen Science Kirk Borne George Mason University School of Physics, Astronomy, & Computational Sciences http://spacs.gmu.edu/ The Problem:
Information Technology R&D and U.S. Innovation
Information Technology R&D and U.S. Innovation Peter Harsha Computing Research Association Ed Lazowska University of Washington Version 9: December 18, 2008 1 Peter Lee Carnegie Mellon University Advances
Data Science at NSF Draft Report of StatSNSF committee: Revisions Since January MPSAC Meeting
Data Science at NSF Draft Report of StatSNSF committee: Revisions Since January MPSAC Meeting Iain Johnstone, Fred Roberts, Co-chairs Apr 2014 1 Subcommittee of MPS AC 1. Introduction Charged by MPS AD
National Nursing Informatics Deep Dive Program
National Nursing Informatics Deep Dive Program What is Nursing Informatics and Why is it Important? Connie White Delaney, PhD, RN, FAAN, FACMI Dean and Professor, School of Nursing Director, Clinical and
Master big data to optimize the oil and gas lifecycle
Viewpoint paper Master big data to optimize the oil and gas lifecycle Information management and analytics (IM&A) helps move decisions from reactive to predictive Table of contents 4 Getting a handle on
From Data to Insight: Big Data and Analytics for Smart Manufacturing Systems
From Data to Insight: Big Data and Analytics for Smart Manufacturing Systems Dr. Sudarsan Rachuri Program Manager Smart Manufacturing Systems Design and Analysis Systems Integration Division Engineering
Data-Intensive Science and Scientific Data Infrastructure
Data-Intensive Science and Scientific Data Infrastructure Russ Rew, UCAR Unidata ICTP Advanced School on High Performance and Grid Computing 13 April 2011 Overview Data-intensive science Publishing scientific
BIG. Big Data Analysis John Domingue (STI International and The Open University) Big Data Public Private Forum
Big Data Analysis John Domingue (STI International and The Open University) Project co-funded by the European Commission within the 7th Framework Program (Grant Agreement No. 257943) 1 The Data landscape
INSIGHTS FROM A FORMER NSF. Rick McCourt, Academy of
INSIGHTS FROM A FORMER NSF PROGRAM DIRECTOR Rick McCourt, Academy of Natural Sciences, Philadelphia PRESENTATION Overview of NSF Searching for Information The Review Process Preparing Competitive Proposals
Exploring the roles and responsibilities of data centres and institutions in curating research data a preliminary briefing.
Exploring the roles and responsibilities of data centres and institutions in curating research data a preliminary briefing. Dr Liz Lyon, UKOLN, University of Bath Introduction and Objectives UKOLN is undertaking
REQUEST FOR PROPOSALS: CENTER FOR LONG-TERM CYBERSECURITY
102 S Hall Rd Berkeley, CA 94720 510-664-7506 [email protected] REQUEST FOR PROPOSALS: CENTER FOR LONG-TERM CYBERSECURITY The University of California, Berkeley Center for Long-Term Cybersecurity (CLTC)
Standards for Big Data in the Cloud
Standards for Big Data in the Cloud International Cloud Symposium 15/10/2013 Carola Carstens (Project Officer) DG CONNECT, Unit G3 Data Value Chain European Commission Outline 1) Data Value Chain Unit
Semantically Steered Clinical Decision Support Systems
Semantically Steered Clinical Decision Support Systems By Eider Sanchez Herrero Department of Computer Science and Artificial Intelligence University of the Basque Country Advisors Prof. Manuel Graña Romay
Environmental Data Management:
Environmental Data Management: Challenges & Opportunities Mohan Ramamurthy, Unidata University Corporation for Atmospheric Research Boulder CO 25 May 2010 Environmental Data Management Workshop Silver
A discussion of information integration solutions November 2005. Deploying a Center of Excellence for data integration.
A discussion of information integration solutions November 2005 Deploying a Center of Excellence for data integration. Page 1 Contents Summary This paper describes: 1 Summary 1 Introduction 2 Mastering
How To Help The European Single Market With Data And Information Technology
Connecting Europe for New Horizon European activities in the area of Big Data Márta Nagy-Rothengass DG CONNECT, Head of Unit "Data Value Chain" META-Forum 2013, 19 September 2013, Berlin OUTLINE 1. Data
Swarthmore College Libraries Digital Collection Development Policy
Swarthmore College Libraries Digital Collection Development Policy March 2015 Table of Contents Introduction Audience Scope Selection Criteria Accession Preservation Access Withdrawal Policy Rights and
Overview of the NSF IUSE Program and Tips for Submi=ng Proposals
Overview of the NSF IUSE Program and Tips for Submi=ng Proposals Kevin M. Lee University of Nebraska & Division of Undergraduate Educa.on (DUE) (EHR) Workshop Outline Overview of IUSE History /EvoluBon
Collaborations between Official Statistics and Academia in the Era of Big Data
Collaborations between Official Statistics and Academia in the Era of Big Data World Statistics Day October 20-21, 2015 Budapest Vijay Nair University of Michigan Past-President of ISI [email protected] What
National Science Foundation Where Discoveries Begin
National Science Foundation Where Discoveries Begin QEM Network Day at NSF for MSIs September 15, 2009 Joanne Tornow, Ph.D. Senior Advisor Biological Sciences Directorate National Science Foundation [email protected]
The Big Data methodology in computer vision systems
The Big Data methodology in computer vision systems Popov S.B. Samara State Aerospace University, Image Processing Systems Institute, Russian Academy of Sciences Abstract. I consider the advantages of
OpenAIRE Research Data Management Briefing paper
OpenAIRE Research Data Management Briefing paper Understanding Research Data Management February 2016 H2020-EINFRA-2014-1 Topic: e-infrastructure for Open Access Research & Innovation action Grant Agreement
NASA s Big Data Challenges in Climate Science
NASA s Big Data Challenges in Climate Science Tsengdar Lee, Ph.D. High-end Computing Program Manager NASA Headquarters Presented at IEEE Big Data 2014 Workshop October 29, 2014 1 2 7-km GEOS-5 Nature Run
Informatics Domain Task Force (idtf) CTSA PI Meeting 02/04/2015
Informatics Domain Task Force (idtf) CTSA PI Meeting 02/04/2015 Informatics Domain Task Force (idtf) Lead Team Paul Harris, Vanderbilt University Medical Center, (co-chair) Steven Reis, University of Pittsburgh
Data Centric Systems (DCS)
Data Centric Systems (DCS) Architecture and Solutions for High Performance Computing, Big Data and High Performance Analytics High Performance Computing with Data Centric Systems 1 Data Centric Systems
BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON
BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON Overview * Introduction * Multiple faces of Big Data * Challenges of Big Data * Cloud Computing
EDITORIAL MINING FOR GOLD : CAPITALISING ON DATA TO TRANSFORM DRUG DEVELOPMENT. A Changing Industry. What Is Big Data?
EDITORIAL : VOL 14 ISSUE 1 BSLR 3 Much has been written about the potential of data mining big data to transform drug development, reduce uncertainty, facilitate more targeted drug discovery and make more
Data Intensive Science and Computing
DEFENSE LABORATORIES ACADEMIA TRANSFORMATIVE SCIENCE Efficient, effective and agile research system INDUSTRY Data Intensive Science and Computing Advanced Computing & Computational Sciences Division University
Tools for Managing and Measuring the Value of Big Data Projects
Tools for Managing and Measuring the Value of Big Data Projects Abstract Big Data and analytics focused projects have undetermined scope and changing requirements at their core. There is high risk of loss
