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Northwest Alliance for Computational Science & Engineering − Oregon State University
How Computer Science Is Revolutionizing Earthquake Engineering
Cherri PancakeNACSE (Oregon State University)
Northwest Alliance for Computational Science & Engineering
Re-Engineering Earthquake Engineering
George E. Brown Jr. Network for Earthquake Engineering Simulation
NSF funding 4 years contruction + 10 years operationExtend national capacity for EE through new facilities
Unique large-scale physical experimentationIntegration of numerical and physical experiments
Create an IT infrastructure thatCaptures and preserves all relevant informationEnables remote participation in real-timeFacilitates re-use of knowledge gained from experiments
Enhance effectiveness of EE researchers through advanced IT
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Northwest Alliance for Computational Science & Engineering
Advanced IT Will Let NEES Researchers …
Control and observe experiments from remote sitesReduce requirement for on-site presence
Gain more from experimentsExploit technology to enhance human observation
Share experiments with colleagues/studentsBroaden participation in experimentsExtend useful lifetime of experimental processes
Exploit corpus of experiment resultsFacilitate re-use of previous experimentationSupport integration of computational and experimental modeling
Northwest Alliance for Computational Science & Engineering
NEES: Distributed Resources and Users
NEESConsortium
ExperimentalData Repositories
ComputationalData Repositories
Unique LaboratoryFacilitiesEquipment
Site 1
EquipmentSite 2
EquipmentSite 3
EquipmentSite 15
. . .
OtherSite A
OtherSite B
Practitioners
EmergencyCommunities
K-14Education
UserCommunities
Earth.Eng.Researchers
NEESgrid
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Northwest Alliance for Computational Science & Engineering
NEES Poses Unique Challenges for IT
DiversityMany types – and scales – of resourcesPrimary (direct) users• Consortium staff and facility managers• Researchers using experimental/computational facilities
Secondary (indirect) users• Other researchers and students• Practitioners• K-12 and general public
DispersalResources distributed nationallyUsers distributed (inter)nationally
Northwest Alliance for Computational Science & Engineering
User Prioritization of IT Needs
Experimental data are reliably captured and stored for future use
IT functionality can be accessed and used with no special trainingEarthquake researchers can find and apply experimental data, easily and accuratelyProject-oriented documentation and discussion tools allow earthquake researchers to plan, conduct, and analyze experiments collaboratively
Clear “Winner”
High priority
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Northwest Alliance for Computational Science & Engineering
How Users Prioritized Needs
Researchers participate in experiments from remote locationsExperiments can span experimental facilitiesSimulation output reliably captured and storedSimulation coupled with experimentationSimulations executed through a shared submission facilityShared tools available for analyzing/mining dataShared tools available for visualizing dataNon-researchers use images/analyses/data for educational and professional purposes
Northwest Alliance for Computational Science & Engineering
What NEES IT Must Deliver
An infrastructure offering:Reliability – no data will be lostUsability – no special expertise or software will be needed to access informationMinimal effort – fast and efficient access to experiments / collaboration mechanisms / dataFlexibility – data archive will provide search/access features to support• Experiment reuse• Comparison/integration of experiments with numerical
simulation
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Northwest Alliance for Computational Science & Engineering
Special Need for Reliability
Experiments must be captured as they happenVery expensiveCan be destructive in nature
Lead researcher must have “instant feedback”Results of one run often used to adjust parameters for next
In future, will need to feed real-time data back to simulation
Northwest Alliance for Computational Science & Engineering
Telepresence: The Raw Ingredients
Sensor data: raw, filtered, graphical summaries10s to 100s of devices operating concurrentlyEventually must scale to 1000s of devices
Data streams from remotely operable cameras and microphones
10s of devices at eye level, suspended from roof, and underwaterIncludes some ultra high-resolution images
Use of computation to merge/analyze real-time data streams
Eventually will use simulation to “play what-if” and change course of experiment
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Without attention to usability ...
Usability essentials:Seamless synchronization of data streams“Intelligent” choice of what to displayNo requirement for user to download softwareFirst experience “pays off” for later ones
Northwest Alliance for Computational Science & Engineering
Engineering the User Experience
Steering and observing a remote experimentResearcher(s) sets up and directs experiment in near-real-timeColleagues from same/other institutions participateObserve/assimilate/discuss varying sets of data streams
Data repositoryarchives all aspects of NEES experiments
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Northwest Alliance for Computational Science & Engineering
Data Reuse: The Raw Ingredients
Extremely large quantities of data must be archived and made publicly available
Storage requirements for video/images will dwarf others Diverse data formats must be integrated
Synchronization markers must be addedMost instruments have no concept of “time”
Metadata will be critical ingredientNeed to be standardized, but no appropriate standards existMust rely on EE researchers for much input
Must be possible to compare experimental data with data from simulations
Northwest Alliance for Computational Science & Engineering
Engineering the User Experience
After-the-fact experiment replayResearcher(s) observe experiment in simulated timeView/analyze subsets of data streams for targeted uses
Single or collaborative researchers search and explore the data repository
So duplication can be eliminatedSo models can be calibratedSo model results can be validated
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Northwest Alliance for Computational Science & Engineering
NEES IT Is Largely a “Human Problem”
“Cast of thousands” (varying disciplinary backgrounds, levels of expertise, durations of involvement)
Massive DataComplex Data
Extreme Scales
Extreme Diversity
Many are new tofacilities sharingremote collaboration
Most are new todata sharingdata standardizationdata preservationcollaboration tools
Virtually all are new to metadata
Northwest Alliance for Computational Science & Engineering
How NEES Will Change EE Research
CaveatsExamples are from tsunami facilityNothing is cast in stone yet
Examples from the Tsunami Wave BasinExperiment setupData modelingMetadata definitionExperiment replayAccess Control
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Northwest Alliance for Computational Science & Engineering
Example 1: Experiment Design
Researchers shouldn’t have to visit site just to get familiar with itKey needs
Learn about facility layout, capabilitiesSee details about equipment and instrumentsPlan layout of models/specimens and instrumentsPlan locations of camerasLearn about setups that worked well for other researchers
Tsunami Wave Basin approachCreate accurate virtual model of labAnimated walkthroughs for newcomersVirtual lab tool for experiment design
Northwest Alliance for Computational Science & Engineering
Example 1: Experiment Design
Virtual wave basin is “better than being there”See future facilitiesFly above scene
“Try out” positionsStudy setups from past tests
Being developed by undergrad Chris Moore and Dr. Metoyer
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Northwest Alliance for Computational Science & Engineering
Example 2: Data Modeling
Team from UIUC used classic approachAbstract, obrject-oriented model
Model for creating specifications (not a specification)Cannot be implemented directly
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Northwest Alliance for Computational Science & Engineering
Usability Considerations
Too hard for engineers to understandPIs will have trouble entering the needed metadataUsers will have trouble understanding how to query database
Doesn’t map well to existing community standards• E.g., tsunami observation standard
Doesn’t interface well to available tools
Goals of revising model for usabilityEasy for both users and developers to understandSupported by commercial and 3rd-party tools• E.g., report generation tools, database systems, data-
mining tools
Northwest Alliance for Computational Science & Engineering
Resource IIResource II
Model Revised for Usability
Each configuration is used for 1+ runs
RunRunRunRun ARunRunRunRunRunRun ARunRunRun A
The lab is a collection of resources
Resource I …
ResourceOutput
Each resource produces output for the run
Each configuration uses a set of resources
ResourceConfigurationResource-
ConfigurationResourceConfiguration
Experiment
An experiment is made up of configurations
…Configuration 3Configuration 2
Configuration 1
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Northwest Alliance for Computational Science & Engineering
Revised ModelExperiments for a project
Expriment at a facility
Experiment has configuration
Multiple test runs for a configuration
Projects publishers and authors
Ack for a project
Roles
Persons project roles
Persons experiment roles
Persons roles
Resource assigned to a config
Configuration of a resource
Type of resourceCalibration for configured resource
Coefficient for calibration
Output from a configured resource
Run has output from resources
Raw data for resource output
Processed output (from raw data)
Project pubs and reports
Calibration for unconfigured resource
Experiment
Project
Facility
Configuration
Run
AuthorsPublishers
AcknowledgementsPersonRole
Role
person
ResourceConfiguration Resource
ResourceTypeCalibrationSet
CalibrationCoefficient
ResourceOutput
RawData
ProcessedOutput
PublicationsReports
Processed data (from processed data)
Northwest Alliance for Computational Science & Engineering
How the Model Is Used
Doesn’t just serve as the basis for database designUsed to integrate all tools and interfaces
“Experiment Notebook” style interfaceMetadata definition toolsSearch-and-query interfaceReport generation toolSame terms will be used in training materials for experimenters
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Northwest Alliance for Computational Science & Engineering
Example 3: Metadata Definition
Metadata is critical to the NEES concepts of sharing and reuseKey issues
Who defines the format?Who creates the metadata for experiments?What are the incentives for providing high-quality metadata?
Tsunami Wave Basin approachMinimize the amount of metadata users must enterEnsure that user is only entering metadata relevant to his/her roleProvide simple-to-use tools that catch as many potential errors as possible
Northwest Alliance for Computational Science & Engineering
Where TWB Metadata Will Come FromResourceConfigurationconfigurationIDresourceIDx0y0z0x1y1z1redgreenbluepatternlabelactiveangleconfigurationFileURIspecialConfigurationnotesacquisitionDeviceacquisitionChannel
<pk><pk>
Metadata about sensor settings for a particular configuration
position of sensor
sensor calibrations
sensor setup
generated automatically
entered by lab tech
entered by PI
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Northwest Alliance for Computational Science & Engineering
Carrot-and-Stick Approach
StickUsers will be required to enter key metadata before experiment can beginPart of agreement with PIs
CarrotMetadata will be used to automatically generate lab reportsPreviously, this was tediously done by hand
We’re asking users to type minimal info ahead of time, rather than typing more after-the-fact
Northwest Alliance for Computational Science & Engineering
Example of Automatic Lab Report
Tsunami Analysis: Experimental and Numerical Modeling of Forces on a Vertical Surface
Generated by a Solitary Wave
byKarl T. Miller
Project Supervisor:Daehyun Yoon
Professor of Civil Engineering
Supported by:National Science Foundation Award No. CMS-51234321
andGeorge E. Brown, Jr., Network for Earthquake Engineering Simulation (NEES)
Tsunami Wave Basin, Oregon State UniversityNational Science Foundation Award No. CMS-0145332
B. Golden Center for Tsunami ResearchDivision of Engineering and Applied Science
University of WhateveritisHollywood, California 80124
Report No. KM-R-95
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Northwest Alliance for Computational Science & Engineering
Example 4: Experiment Replay
Long-term archiving of experimental data is central to NEESKey needs
Ability to understand what experimenter was trying to doAccess to all details of instrumentation and experiment designAbility to accurately “replay” what happenedAbility to search data using fuzzy criteria, e.g.• Experiments with “similar results”• Experiments “like this one”
Tsunami Wave Basin approachSearch-and-query tool that “understands” experimentsLab notebook style tool for viewing dataEasy downloading
Northwest Alliance for Computational Science & Engineering
Example 4: Experiment Replay
Lab notebook provides access to everything (even operator comments)
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Northwest Alliance for Computational Science & Engineering
Example 5: Access Control
Sharing of data is central to NEESKey issues
Researchers need to be first to publishTradeoffs between privacy and shared useTradeoffs between open access and “safe” use of data
Tsunami Wave Basin approachAccess control variesMore data becomes open over time
Northwest Alliance for Computational Science & Engineering
Example 5: Access Control
Some degree of access guaranteed for all NEES experiments
Read high-level descriptive metadataView video from “lab-sweep” camerasView low-resolution video from experiment camerasView selected, partial-resolution data streamsView lab “chat” channel
PI controls who has access to other information, e.g.View full-/partial-resolution data or camera outputControl camerasRead or write in lab notebookWrite to lab “chat” channelAssign privileges to other users (delegation)
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Northwest Alliance for Computational Science & Engineering
Example 5: Access Control
Simple interface for PIs
Northwest Alliance for Computational Science & Engineering
Conclusions
First-ever IT infrastructure is central to NEES conceptKey challenges have to do with the human element
Diverse usersBroad range of skill sets and familiarity with advanced ITBroad spectrum of needsExtremely high expectations (!)
Ultimate success/failure rides on whether users can access data in ways that are
“Natural”UsefulSafe
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Tsunami Wave Basin softwarebeing developed by undergrads,
grads, and research staff at NACSE
www.nacse.org/nees/
Who Deserves the Credit
CS Faculty/ResearchersRon MetoyerJon HerlockerSally HaererKen FerschweilerTim HoltJoe HanusJason McKerrLeanne Lai
CS GradsDae YoonAnton DragunovSeikyung Jung
CS UndergradsChris MooreMichiko TakedaTammy CulterKami Vaniea
ECETodd Shechter
CCEEHarry YehDan CoxSolomon YimDave StandleyTerry Dibble