1:30 Break Out Session I: Roadmap first capability Facilitators 2:30 2:45 Out Session I: Roadmap first capability Facilitators 4:30 Break / Poster Session Setup 4:45 Poster Session AIST PIs 6:30 Adjourn 6:30 Facilitator tag-up Facilitators 7:00 Facilitators Adjourn 2. Agenda, 10 February 2010 Time Topic Leader 8:00 Decadal Survey Mission Perspectives II: Needs & Use Cases ACE HyspIRI Simone Tanelli Steve Chien 8:30 Decadal Survey Era Themes II: Inter-Disciplinary Users Carbon Hydrological Modeling Data Lineage Computing Challenges Petr Voltava Ken Harrison Greg Leptoukh Dan Duffy 9:30 Panel Discussion Karen Moe 10:00 Break 10:15 Break Out Session II: Review/discuss Capabilities Finish roadmapping activities from yesterday. Facilitators 11:15 Break Out Session II: Create roadmaps for other Needs. Facilitators 12:00 Lunch Break 1:30 Break Out Session II: Continue roadmapping. Facilitators 3:30 3:45 Out Session II: Continue roadmapping. Facilitators 4:45 Out Session II: Roadmap review. Facilitators 5:30 Adjourn 5:30 Facilitator tag-up Facilitators 6:00 Facilitators Adjourn 3. Agenda, 11 February 2010 Time Leader 8:00 Break Out Session III: Finalize roadmaps Create out brief Facilitators 10:00 Break 10:15 Break Out Session Results Sensor Support Advanced Data Processing Data Services Group designee 11:30 Conclusion Workshop Report Schedule Wrap Up Comments Karen Moe 12:00 Adjourn
Table 10. Technology Alternatives for the Capability, On-board Special Product Generation and Dissemination Technology Alternative Flight Software Development Framework (Science/Instrument) Flight Software Development Framework (Spacecraft C&C) Automation of Control of the On-board Flexible Framework On-board Processor Type I (Multi-core CPUs) Processor Type II (Reconfigurable Processors, SOCs) Technology Area Data Collection & Handling Data Collection & Handling Data Collection & Handling Data Collection & Handling Data Collection & Handling Technology Category Flight Software Ground Software On-Board Processing Hardware Description Develop a software development framework to develop and compile the algorithms and applications that will run on the on-board processor to generate quick-look data products. Technology to support and facilitate autonomous operation and on-demand tasking. Framework means software to sustain flight development. Technology steps: (1) identify core algorithmic functions/use cases; (2) identify accuracy requirements [2011, TRL 3]; (3) develop application programming interfaces and standards for instrument data processing functions; (4) develop capability to integrate and chain processing steps [2013; TRL 4]; (5) develop mechanism to implement code generation for each target platform [2014]; (6) perform V&V for hardware-in-loop as part of the code acceptance process [2014, TRL 6]. Capability for processor to support event based response. On-board software has a representation of the mission plan and can autonomously modify it when appropriate (e.g., based on observations, possibly from other platforms relevant to non-mapping missions). A mechanism to automate management of algorithms - e.g., scheduling of items in column one. Includes allocation of elements to the flight and ground segments. Could also include control of the ground processing chain Develop and assess multi-core processors and associated code. (assumes traditional /manual programming - no automatic parallelization) Develop a high performance reconfigurable data processor. Technology challenge is how to make these devices operate reliably in the space environment. TRL Assessment TRL in 2010 2 6 7 2 4 Intermediate TRL 5 7 8 4 7 Intermediate Date 2014 2014 2012 2014 2014 Intermediate Success Criteria Reduction in of Effort (LOE) required to develop flight instrument software while maintaining reliability and safety Capability of autonomous nominal commanding of the spacecraft TBD increase in algorithm performance over current TBD increase in algorithm performance over current art
Exit TRL 7 9 8/9 6 8 Exit Date 2018 2018 2014 2020 2020 Exit Success Criteria Intermediate success criteria applied to or reconfigurable systems Intermediate success criteria with multiple contingencies autonomous operation of product generation for a given policy TBD increase in algorithm performance over current TBD increase in algorithm performance over current Critical Milestones & Dates Mission Concept Review (MCR) Risk (L/M/H) Medium High Low Medium Medium Risk Comments Building a library is an intermediate step with lower risk than creating an integrated environment. Requires very high reliability - i.e., it is expensive. A subset of automated operations have been demonstrated on several missions. Software development for parallel processing is well established, but multi-core is a new platform. Some initial activities have been started in this area. More work in space qualification is still needed. Cost (L/M/H) High High Medium High High Cost Comments Dependency Comments & Notes Success criteria: Performance requirements will be driven by the types of data products that NASA would like to produce, the specific mission / instrument, and latency requirements. Success criteria: Performance requirements will be driven by the of data products that NASA would like to produce, the specific mission / instrument, and latency requirements based
Based 12. Technology Categories for the Capability, Mission Autonomy Technology Category Autonomous Mission Planning & Scheduling Event Detection and Tracking Multi-Asset Coordination Smart Monitoring, Failure Analysis, and Recovery Delivery of Products via Autonomous Space/Ground Coupled Systems Technology Area Systems Management, Data Collection and Handing Data and Information Production, Data Collection and Handling Systems Management, Data Collection and Handing Systems Management, Data and Information Production, Data Collection & Handling Transmission and Dissemination Technology Alternatives Hybrid Approaches, Operations Research, AI-Based Approaches Machine Learning-Based, Physics-Based, Approaches Hierarchical Control, Market Oriented, Distributed Control (Emergent Behavior) Evolutionary Approaches Machine-Learning, (Physics), Traditional Limit-Based, Bayesian/Probabilistic Approaches Workflow Engines, and Semantic Web Approaches Description Perform science while respecting mission operation constraints including retasking. Note that "search, access, analysis, and display (data mining) could be enhanced by this capability where one could resolve missing data by making autonomous requests to spacecraft assets. Detecting and tracking of events that feed into continuous part of planning process Support to automatically negotiate multiple system and spacecraft resources to support observation objectives (e.g. Sensor Webs) Enable continuous operations despite hardware and software failures and more rapid recovery from the above. Ability of system to detect and correct for instrument anomalies amenable to autonomous changes in observation plan. Ability to request custom data products fulfilled autonomously by tasking, data processing, etc. TRL Assessment TRL in 2010 6 4 3 2 5 Intermediate TRL 7 5 5 6 7 Intermediate Date 2015 2015 2018 2017 2014 Intermediate Success Criteria Exit TRL 8 7 7 8 8 Services, Based
Table 14. Technology Categories for the Capability, Multi-scale Spatial and Temporal Calibration/Validation Support Technology Category Uncertainty Quantification Methods Database of Ancillary Information Multi-Sensor Data Fusion In-Situ Sensor Networks Technology Area Data and Information Production Data and Information Production Data and Information Production Data Collection and Handling, System Management Technology Alternatives Estimating Probability Distributions Technique Web Services and workflows Mosaicing, Data Assimilation model as underlying driver), Aggregation SensorKit System, SoilSCAPE Description Technology to quantify error in instrument measurements. Experiment design to improve validation. Weather, temperature and other environmental measurement data requiring resolution of temporal and spatial data (data access). Includes infrastructure to establish the proper database technology (standards for collation, search, access) for Cal/Val operations. Technology to fuse data products (and perform georectification) across sensors within a known global error bound Provides a trusted ground source for reducing uncertainty in data products TRL Assessment Entry TRL in 2010 4 5 4 4 Intermediate TRL 6 7 6 7 Intermediate Date 2015 2014 2015 2015 Intermediate Success Criteria Exit TRL 9 9 9 9 Exit Date 2022 2022 2022 2022 Exit Success Criteria Error analysis results are quantitative and trusted Environmental parameter data easily accessible and integrated into Cal/Val analysis system Fused data products are accurate within trusted error analysis metrics Easily deployable in-situ sensor-web capability available and tailored to observational assets
Exit Success Criteria Verified and validated algorithms with demonstrated performance to support DS era data volumes implemented as software libraries. The group discussed the need to evaluate these new algorithms by assessing their impact on science and decision making. Release to the science community of an open source Data Fusion software library. Specific quantitative performance metrics should be defined that provide the necessary throughput such that the DS era data volumes could be processed in a reasonable amount of time. Other Critical Milestones & Dates None None Technical Risk (L/M/H) Medium Low Risk Comments There are well-defined methods to be explored so the main risk is implementing these as efficient algorithms that can support different data fusion algorithms that scale to the DS era data volumes. Development of software libraries is relatively low risk, however the main risk is schedule risk due to the possibly time consuming testing and performance optimization. Cost Risk (L/M/H) Medium Medium Cost Comments The mathematical method development and translation into algorithms will be labor intensive and require a sustained effort. Testing and scaling assessment of the algorithms could increase the overall cost. While low risk, this is a labor intensive effort due to extensive testing and performance tuning required for HPC software libraries. Dependency Comments & Notes The mathematical methods and algorithm development needs to feed the other technology development task of translating the verified and validate algorithms into reusable software libraries. This task requires that the companion task reach a certain level of maturity so that verified and validated algorithms are available to being building the software library.
Other Critical Milestones & Dates None None Technical Risk (L/M/H) Medium Low Risk Comments Risks include extending current techniques that focus on time-series data only to high dimensions that include spatial, temporal and spectral degrees of freedom, and developing of new techniques that leverage modern approaches to machine learning and AI. The handling of this high dimensional data adds to the challenge of developing efficient algorithms that can support either pipelined or parallelized implementation. Translation of well developed algorithms into reusable software library should be relatively straight forward. Cost Risk (L/M/H) Medium Medium Cost Comments The development of new methods and extension of existing methods will require sustained levels of funding. Algorithm V&V will also add to overall cost. However, development costs will provide significant benefits to the Earth science community by providing a validated capability that can be widely used. While low risk, this is likely to require an intensive effort with considerable software testing adding to the overall cost. Dependency Comments & achieving the overall Data Mining capability will require translation of these algorithms into a high performance reusable software library as described in the companion technology. This technology development is closely coupled with the companion task of developing new data mining algorithms.
Table 22. Technology Alternatives for the Earth Science Workflow Virtualization Capability Technology Alternative User Environment for Workflow Creation & Execution Middleware for Resource Virtualization Resource Scheduling and Management Technology Category Workflow Environment Software Middleware Schedulers and Resource Management Technology Area Data and Information Production Data and Information Production, System Management System Management, Data and Information Production Description A framework and user environment for the creation and execution of scientific workflows. The framework provides the basic development environment and common library for creating, executing and managing workflows. Allows users to create their own software modules and function as part of the workflow. Provides interfaces to middleware that virtualizes the IT and data resources necessary to support the Earth Science workflows. Middleware (software) that provides the virtualization and links between the User Interface and Workflow development environment and the IT resources that are being virtualized (HPC, storage, data, etc.). This middleware should also interface with the necessary resource scheduling and management services that provide the users the ability to schedule and management workflow execution. The middleware should also provide some level of fault-management in workflow execution. The middleware provides the translation from user’s logical workflow specification, including data and services, to the physical instantiation of these entities. Scheduling and resource management software and interfaces. Provides users the ability to schedule, monitor and manage their workflow on the front-end. The back-end works with the middleware to schedule specific HPC resources to execute user workflow, report problems and provide fault-tolerant execution. TRL Assessment Entry TRL in 2010 3 3 3 Intermediate TRL 5 5 5 Intermediate Date 2013 2012 2012
29. Technology Alternatives for the Data Assimilation Framework Capability Technology Alternative Data Assimilation Framework User Interface and Development Environment Automated Interface Builder Middleware and Reusable/Extensible Software Library for Data Assimilation Technology Category Software Frameworks, user interfaces Software Interfaces & Components (APIs) Middleware, Software Libraries Technology Area Data and Information Production Data and Information Production Data and Information Production Description Technologies involve the development of an easy-to-use user interface and development environment for science users. The user interface allows the user to build the data assimilation interfaces between user specified observational data sets and models. The framework should allow the user to specify data assimilation workflow for test and execution, and perform job scheduling and monitoring for workflow execution Technology involves the development of interface specification standards and the development of tools to build the necessary interfaces between observational data and the physical models. Different approaches to the interface building tools should be investigated, as well as standard model API specifications. Technology involves development of reusable and extensible software libraries for various aspects of the data assimilation problem space, e.g., implementation of different assimilation algorithms. The software should be modular with functionalities accessible through an API from a set of relevant programming languages TRL Assessment Entry TRL in 2010 3 2 3 Intermediate TRL 5 4 4 Intermediate Date 2014 2013 2013 Intermediate Success Criteria User interface and development environment verified with users; ability to create workflow based on user defined use cases Baseline approach to automated interface building defined and tested with at least two widely used models as verification. The baseline approach may involve methods to use model APIs or function calls and an IDL-like capability. Baseline middleware sufficient to demonstrate full end-to-end integration of the user interface and execute some example use cases for data assimilation using Tier 1 DS mission sample data sets. Exit TRL 7 7 7 Exit Date 2016 2016 2016
Table 34. Technology Alternatives for the Capability, Data Discovery and Access Technology Alternative Single Catalog + Semantics + Data Quality Multiple Catalogs + Semantics + Data Quality Semantic Search on Ontologies Search Indexing Search Virtual Products Technology Category Search Search Search Search Search Search Description or controlled vocabulary search (single catalog) federated keyword search across multiple catalogs search that uses contextual info and domain knowledge captured in an ontology search that uses contextual info (on demand), retrieves results related by higher-level concepts on the fly harvesting of different content types including higher-level concepts searching for virtual products which may include data/images that have not been acquired or products that do not exist yet; includes getting status of sensors and timeframes, etc. Entry TRL in 2010 3 3 3 2 2 3 Intermediate TRL 5 5 5 3 4 5
Intermediate Date 2012 2012 2012 2012 2012 2012 Intermediate Success Criteria for a selected domain: relevance, precision, increased breadth of data, timeliness, ease of use, use of inferred relationships for a selected domain: relevance, precision, increased breadth of data, timeliness, ease of use, use of inferred relationships for a selected domain: relevance, precision, increased breadth of data, timeliness, ease of use, use of inferred relationships relevance and precision of search results in a selected domain relevance and precision of search results in a selected domain retrieve future products that are dependent on 1-2 data sets Exit TRL 6 6 6 4 7 6 Exit Date 2014 2014 2014 2014 2014 2014 Exit Success Criteria relevance, precision, increased breadth of data, timeliness, ease of use, use of inferred relationships relevance, precision, increased breadth of data, timeliness, ease of use, use of inferred relationships relevance, precision, increased breadth of data, timeliness, ease of use, use of inferred relationships working system for a selected domain implemented in a commercial search engine (e.g., Google) search for and retrieve workflows that generate a customized virtual product Risk (L/M/H) Medium Medium Medium High Medium Medium
Risk Comments community acceptance of ontology and quality indicators, done to some extent community acceptance of ontology and quality indicators, done to some extent community acceptance of ontology and quality indicators, done to some extent requires additional enhancements in AI possible collaboration with commercial search engine provider to mitigate risk; risk a function of commercial provider Cost (L/M/H) Medium Medium Medium High High Medium Cost Comments Knowledge engineering Knowledge engineering labor Knowledge engineering labor current approaches are too Shared by commercial provider