AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
•Télécharger en tant que PPTX, PDF•
1 j'aime•418 vues
Ilya Zaslavsky, David Valentine, Amarnath Gupta, Stephen Richard, Tanu Malik Presentation given in the afternoon Architecture Forum Session on Day 1, June 24 at the EarthCube All-Hands Meeting
![Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences
The Science Enterprise
• Ask questions
• Collect information
• Formulate hypotheses
• Test hypotheses to
determine which (if any)
provide satisfactory answer
• Document, curate, and
disseminate data and
results.
…. AND INCREASINGLY:
• Integrate data, analyses,
models across domains
• Collaborate: leverage pooled expertise and resources
increasing amount of data produced in modern science. LSDMA
bridges the gap between data production and data analysis using
a novel approach by combining specific community support and
generic, cross community development. In the Data Life Cycle
Labs (DLCL) experts from the data domain work closely with
scientific groups of selected research domains in joint R&D
where community-specific data life cycles are iteratively
optimized, data and meta-data formats are defined and
standardized, simple access and use is established as well as data
and scientific insights are preserved in long-term and open
accessible archives.
Keywords: data management, data life cycle, data intensive
computing, data analysis, data exploration, LSDMA, support, data
infrastructure
I. INTRODUCTION
Today data is knowledge – data exploration has become the
4th pillar in modern science besides experiment, theory, and
simulation as postulated by Jim Gray in 2007 [1]. Rapidly
increasing data rates in experiments, measurements and
simulation are limiting the speed of scientific production in
various research communities and the gap between the
generated data and data entering the data life cycle (cf. Fig1) is
widening. By providing high performance data management
components, analysis tools, computing resources, storage and
services it is possible to address this challenge but the
realization of a data intensive infrastructure at institutes and
universities is usually time consuming and always expensive.
The introduced “Large Scale Data Management and Analysis”
(LSDMA) project extends the services for research of the
Helmholtz Association of research centers in Germany with
community specific Data Life Cycle Laboratories (DLCL). The
The LSDMA project initiated at the Karlsruhe Institute of
Technology (KIT), builds on the familiarity with supporting
local scientists at a computer center, the knowledge of running
the Grid Computing Centre Karlsruhe (GridKa) [2] as the
German Tier 1 hub in the World Wide LHC Computing
infrastructure [3], the Large Scale Data Facility (LSDF) [4] and
the experience with the very successful Simulation Labs [5]
that specialize at supporting HPC users.
Figure 1. The scientific data life cycle](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
Similaire à AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces (20)
Plus de EarthCube
Plus de EarthCube (20)
Dernier
Dernier (20)
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
- 1. EarthCube Conceptual Design: Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences http://workspace.earthcube.org/transformative-research-collaboration ILYA ZASLAVSKY, DAVID VALENTINE, AMARNATH GUPTA San Diego Supercomputer Center/UCSD STEPHEN RICHARD Arizona Geological Survey TANU MALIK University of Chicago
- 2. Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences The Science Enterprise • Ask questions • Collect information • Formulate hypotheses • Test hypotheses to determine which (if any) provide satisfactory answer • Document, curate, and disseminate data and results. …. AND INCREASINGLY: • Integrate data, analyses, models across domains • Collaborate: leverage pooled expertise and resources increasing amount of data produced in modern science. LSDMA bridges the gap between data production and data analysis using a novel approach by combining specific community support and generic, cross community development. In the Data Life Cycle Labs (DLCL) experts from the data domain work closely with scientific groups of selected research domains in joint R&D where community-specific data life cycles are iteratively optimized, data and meta-data formats are defined and standardized, simple access and use is established as well as data and scientific insights are preserved in long-term and open accessible archives. Keywords: data management, data life cycle, data intensive computing, data analysis, data exploration, LSDMA, support, data infrastructure I. INTRODUCTION Today data is knowledge – data exploration has become the 4th pillar in modern science besides experiment, theory, and simulation as postulated by Jim Gray in 2007 [1]. Rapidly increasing data rates in experiments, measurements and simulation are limiting the speed of scientific production in various research communities and the gap between the generated data and data entering the data life cycle (cf. Fig1) is widening. By providing high performance data management components, analysis tools, computing resources, storage and services it is possible to address this challenge but the realization of a data intensive infrastructure at institutes and universities is usually time consuming and always expensive. The introduced “Large Scale Data Management and Analysis” (LSDMA) project extends the services for research of the Helmholtz Association of research centers in Germany with community specific Data Life Cycle Laboratories (DLCL). The The LSDMA project initiated at the Karlsruhe Institute of Technology (KIT), builds on the familiarity with supporting local scientists at a computer center, the knowledge of running the Grid Computing Centre Karlsruhe (GridKa) [2] as the German Tier 1 hub in the World Wide LHC Computing infrastructure [3], the Large Scale Data Facility (LSDF) [4] and the experience with the very successful Simulation Labs [5] that specialize at supporting HPC users. Figure 1. The scientific data life cycle
- 3. Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences Design Framework: Federation of Systems Research enterprise includes subsystems at the project, program and agency level, many of which are independent of NSF • Requirements are a moving target • Emergent behavior is to be expected • Technology is constantly changing • Community governance within constraints of funding agencies • Evolutionary process and adaptation: • Lots of variation; Mechanism to select ‘fittest’; Composability • Technology must foster delegation of responsibilities and communication: • Promote self-organization, Cultivate ideas, Maintain feedback between subsystems • Reliability: responsiveness, robustness, correctness • Identity of system is based on shared goals and practices
- 4. Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences Communication loops Bottom-up Studies Top-down Studies Cross-Domain Scientists Trends and Patterns Data interoperability best practices Scientific Governance Success stories Technical Governance Data Providers Feasibility Priorities Strategies Data Products Options Costs Problems and issues Related work Questions and clarifications Questions and clarifications
- 5. Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences Communication metrics
- 6. Components and Perspectives on EarthCube
- 7. Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences Converging on reference architecture semantics Analysis of existing building blocks, and their variability Component System Function Description Interfaces Implementation Steward Organization Availability Reference Developing cross-domain vocabularies, connecting domain models
- 8. Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences Requirements Process Workshop Summaries Surveys Architecture Designs Analyze what worked Incorporate social technologies Inventory CI building blocks
- 9. Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences Concerns Hitting the right level of granularity in the design Identifying necessary communication channels Account for all key perspectives Fixing the scope and technologies Balancing current and future requirements Harmonizing technical and social subsystems and managing interactions between them Uneven standardization and convergence across domains and functional components Constructing a self-organizing plug-and-play system Inventorying building blocks
- 10. Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences Summary System is defined by: Specifications for interfaces and interchange formats (the gateways) Definition of key functional components at an abstract level Discovery, Workflow s, Data processing, annotation, documentation Technology needs to support Communication between subsystems (people and machines) Collection of metrics required to assess what is working (selection of the fittest) Assembly of components