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AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces
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AHM 2014: Enterprise Architecture for Transformative Research and Collaboration Across Geoscinces

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Ilya Zaslavsky, David Valentine, Amarnath Gupta, Stephen Richard, Tanu Malik …

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

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  • 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

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