National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Architecting Scientific Data Systems in the 21st
Century
Dan Crichton
Principal Computer Scientist
Program Manager, Data Systems and Technology
NASA Jet Propulsion Laboratory
DJC-1
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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Architecting the “End-to-End” Science Data
System
Focus on
– science data generation
– data capture, end-to-end
– access to science data
by the community
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Multiple scientific domains
– Earth science
– Planetary science
– Biomedical research
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Applied technology
research
– SW/Sys architectures
– Product lines
– Emerging technologies
DJC-2
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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Challenges in Science Data Systems
A major challenge is in organizing the wealth of science data which
requires both standards and data engineering/curation
– Search and access are dependent on good curation
– Community support is critical to capture and curate the data in a manner
that is useful the community
Usability of data continues to be a big challenge
– Planetary science requires ALL science data and/or science data pipelines
be peer reviewed prior to release of data
– Standard formats are critical
Data sharing continues to be a challenge
– Policies at the grant level coupled with standard data management plans
are helping
Computational and Storage, historically major concerns, are now
commodity services
– Google, Microsoft Research, Yahoo! And Amazon try to provide services to
e-science in the form of “Cloud Computing”
DJC-3
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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National Research Council: Committee on
Data Management and Computation
CODMAC (1980s) identified seven core principles:
– Scientific involvement;
– Scientific oversight;
– Data availability including usable formats, ancillary data, timely distribution,
validated data, and documentation;
– Proper facilities;
– Structured, transportable, adequately documented software;
– Data storage in permanent and retrievable form; and
– Adequate data system funding.
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The CODMAC has led to national efforts to organize scientific results in
partnership with the science community (particularly physical science)
What does CODMAC mean in the 21st Century?
DJC-4
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
The “e-science” Trend…
Highly distributed, multi-organizational systems
– Systems are moving towards loosely coupled systems or federations in
order to solve science problems which span center and institutional
environments
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Sharing of data and services which allow for the discovery, access, and
transformation of data
– Systems are moving towards publishing of services and data in order to
address data and computationally-intensive problems
– Infrastructures which are being built to handle future demand
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Address complex modeling, inter-disciplinary science and decision
support needs
– Need a dynamic environment where data and services can be used quickly
as the building blocks for constructing predictive models and answering
critical science questions
5
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
JPL e-science Examples
Planetary Data System
Distributed Planetary Science Archive
Rings Node
Ames Research Center
Moffett Field, CA
Geosciences Node
Washington University
St. Louis, MO
Imaging Node
JPL and USGS
Pasadena, CA and Flagstaff, AZ
THEMIS Data Node
Arizona State University
Tempe, AZ
Central Node
Jet Propulsion Laboratory
Pasadena, CA
Small Bodies Node
University of Maryland
College Park, MD
Atmospheres Node
New Mexico State University
Las Cruces, NM
Planetary Plasma Interactions Node
University of California Los Angeles
Los Angeles, CA
Navigation Ancillary Information Node
Jet Propulsion Laboratory
Pasadena, CA
EDRN Cancer Research (8X)
• Highly diverse (30+ centers performing
parallel studies using different instruments)
• Geographically distributed
• New centers plugging in (i.e. data nodes)
• Multi-center data system infrastructure
• Heterogeneous sites with common
interfaces allowing access to distributed
portals
Integrated based on common data standards
Secure (e.g. encryption, authentication,
authorization)
Planetary Science Data System (4X)
• Highly diverse (40 years of science data
from NASA and Int’l missions)
• Geographically distributed; moving int’l
• New centers plugging in (i.e. data nodes)
• Multi-center data system infrastructure
• Heterogeneous nodes with common
interfaces
• Integrated based on enterprise-wide data
standards
• Sits on top of COTS-based middleware
National Data Sharing Infrastructure
Supporting Collaboration In Biomedical Research For EDRN
Fred Hutchinson
Cancer Research Center, Seattle
(DMCC)
Creighton
University
(CEC)
University
of Michigan
(CEC)
University of
Pittsburgh
(CEC)
University of
Colorado
(CEC)
UT Health Science
Center, San Antonio
(CEC)
Moffitt Cancer
Center, Tampa
(BDL)
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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Architectural drivers in science data systems
Increasing data volumes requiring new
approaches for data production,
validation, processing, discovery and
data transfer/distribution (E.g., scalability
relative to available resources)
Increased emphasis on usability of the
data (E.g., discovery, access and
analysis)
Archive Volume Growth
90
Increasing diversity of data sets and
complexity for integrating across
missions/experiments (E.g., common
information model for describing the
data)
80
70
TB (Accum)
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60
50
TBytes
40
30
20
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Increasing distribution of coordinated
processing and operations (E.g.,
federation)
Increased pressure to reduce cost of
supporting new missions
Increasing desire for PIs to have
integrated tool sets to work with data
products with their own environments
(E.g. perform their own generation and
distribution)
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0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Year
Planetary Science Archive
DJC-7
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Architectural Focus
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Consistent distributed capabilities
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Develop on-demand, shared services (E.g. processing, translation, etc)
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Deploy high throughput data movement mechanisms
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Move capability up the mission pipeline
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Reduce local software solutions that do not scale
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Build value-added services and capabilities on top of the infrastructure
– Resource discovery (data, metadata, services, etc), unified repository
access, simple transformations, bulk transfer of multiple products, and
unified catalog access
– Move towards era of “grid-ing” loosely coupled science system
– Processing
– Translation
– Increasing importance in developing an “enterprise” approach with common
services
DJC-8
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
Started in 1998 as a research and
development task funded at JPL by the Office
of Space Science to address
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Object Oriented Data Technology*
Application of Information Technology to Space
Science
Provide an infrastructure for distributed data
management
Research methods for interoperability,
knowledge management and knowledge
discovery
Develop software frameworks for data
management to reuse software, manage risk,
reduce cost and leverage IT experience
OODT/Science
Web Tools
Archive
Client
Navigation
Service
OBJECT ORIENTED DATA TECHNOLOGY FRAMEWORK
Archive
Service
Profile
Service
Product
Service
Query
Service
Bridge to
External
Services
Other
Service 1
Other
Service 2
Profile
XML
Data
Data
System
1
Data
System
2
OODT Initial focus
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Data archiving – Manage heterogeneous data
products and resources in a distributed,
metadata-driven environment
Data location and discovery – Locate data
products across multiple archives, catalogs and
data systems
Data retrieval – Retrieve diverse data products
from distributed data sources and integrate
* 2003 NASA Software of the
Year Runner Up
DJC-9
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
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Architectural Principles*
Separate the technology and the information architecture
Encapsulate the messaging layer to support different messaging
implementations
Encapsulate individual data systems to hide uniqueness
Provide data system location independence
Require that communication between distributed systems use metadata
Define a model for describing systems and their resources
Provide scalability in linking both number of nodes and size of data sets
Allow systems using different data dictionaries and metadata
implementations to be integrated
Leverage existing software, where possible (e.g., open source, etc)
* Crichton, D, Hughes, J. S, Hyon, J, Kelly, S. “Science Search and Retrieval using XML”,
Proceedings of the 2nd National Conference on Scientific and Technical Data, National Academy of Science,
Washington DC, 2000.
DJC-10
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
1. Science data tools and
applications use “APIs”
to connect to a virtual data
repository
Distributed Architecture
2. Middleware creates the
data grid infrastructure
connecting distributed
heterogeneous systems and
data
Mission
Data
Repositories
OODT
API
Visualization Tools
OODT
API
Web Search Tools
OODT
API
Analysis Tools
3. Repositories for
storing and retrieving
many types of data
OODT
Reusable
Data
Grid
Framework
Biomedical
Data
Repositories
Engineering
Data
Repositories
DJC-11
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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Software Implementation
OODT is Open Source
Developed using open source software (i.e. Java/J2EE and XML)
Implemented reusable, extensible Java-based software components
– Core software for building and connecting data management systems
Provided messaging as a “plug-in” component that can be replaced
independent of the other core components. Messaging components include:
– CORBA, Java RMI, JXTA, Web Services, etc
– REST seems to have prevailed
Provided client APIs in Java, C++, HTTP, Python, IDL
Simple installation on a variety of platforms (Windows, Unix, Mac OS X, etc)
Used international data architecture standards
– ISO/IEC 11179 – Specification and Standardization of Data Elements
– Dublin Core Metadata Initiative
– W3C’s Resource Description Framework (RDF) from Semantic Web Community
DJC-12
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
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Often unique, one of a kind missions
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Highly distributed acquisition and processing across
partner organizations
Highly diverse data sets given heterogeneity of the
instruments and the targets (i.e. solar system)
Missions are required to share science data results
with the research community requiring:
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Can drive technological changes
Instruments are competed and developed by
academic, industry and industrial partners
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Characteristics of Informatics in Space
Science
Common domain information model used to drive
system implementations
Expert scientific help to the user community on using
the data
Peer-review of data results to ensure quality
Distribution of data to the community
Planetary science data from NASA (and some
international) missions is deposited into the
Planetary Data System
DJC-13
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Distributed Space Architecture
DJC-14
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
Planetary Science Data Standards
JPL has led and managed
development of the planetary
science data standards for NASA
and the international community
– ESA, ISRO, JAXA, etc leveraging
planetary science data standards
– A diverse model used across the
community that unifies data
systems
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Core “information” model that has
been used to describe every type
of data from NASA’s planetary
exploration missions and
instruments
– ~4000 different types of data
PDS Image Class (Object-Oriented)
PDS Image
Label (ODL)
Describes
An Image
DJC-15
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
Pre-Oct 2002, no unified view across distributed
operational planetary science data repositories
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Traditional distribution infeasible due to cost and
system constraints
Mars Odyssey could not be distributed using traditional
method
Current work with the OODT Data Grid Framework
has provided the technology for NASA’s planetary
data management infrastructure to
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Science data distributed across the country
Science data distributed on physical media
Planetary data archive increasing from 4 TBs in
2001 to 100 TBs in 2009
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2001 Mars Odyssey: A paradigm change
2001 Mars Odyssey
Support online distribution of science data to planetary
scientists
Enable interoperability between nine institutions
Support real-time access to data products
Provided uniform software interfaces to all Mars
Odyssey data allowing scientists and developers to link
in their own tools
Operational October 1, 2002
Moving to multi-terrabyte online data movement in
2009
DJC-16
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Explosion of Data in Biomedical Research
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“To thrive, the field that links biologists and their data
urgently needs structure, recognition and support. The
exponential growth in the amount of biological data
means that revolutionary measures are needed for data
management, analysis and accessibility. Online
databases have become important avenues for
publishing biological data.” – Nature Magazine,
September 2008
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The capture and sharing of data to support
collaborative research is leading to new opportunities to
examine data in many sciences
– NASA routinely releases “data analysis programs”
to analyze and process existing data
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EDRN has become a leader in building informatics
technologies and constructing databases for cancer
research. The tools and technologies are now ready
for wider use!
27-Jun-16
EDRN Data
Repositories
DJC-17
17
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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Bioinformatics: National Cancer Institute
Early Detection Research Network (EDRN)
Initiated in 2000, renewed in 2005
100+ Researchers (both members and
associated members)
~40 + Research Institutions
Mission of EDRN
– Discover, develop and validate biomarkers for
cancer detection, diagnosis and risk assessment
– Conduct correlative studies/trials to validate
biomarkers as indicators of early cancer, preinvasive cancer, risk, or as surrogate endpoints
– Develop quality assurance programs for
biomarker testing and evaluation
– Forge public-private partnerships
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Leverage building distributed planetary science
data systems for biomedicine
DJC-18
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
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EDRN has been a pioneer in the use of
informatics technologies to support biomarker
research
EDRN has developed a comprehensive
infrastructure to support biomarker data
management across EDRN’s distributed cancer
centers
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EDRN Knowledge Environment
Twelve institutions are sharing data
Same architectural framework as planetary science
It supports capture and access to a diverse set
of information and results
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Biomarkers
Proteomics
Biospecimens
Various technologies and data products (image,
micro-satellite, …)
Study Management
DJC-19
National Aeronautics and
Space Administration
EDRN’s Ontology Model
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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EDRN has developed a High level ontology model for
biomarker research which provides standards for the
capture of biomarker information across the enterprise
Specific models are derived from this high level model
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Model of biospecimens
Model for each class of science data
EDRN CDE Tools
EDRN is specifically focusing on a granular model for
annotating biomarkers, studies and scientific results
EDRN has a set of EDRN Common Data Elements
which is used to provide standard data elements and
values for the capture and exchange of data
DJC-20
EDRN Biomarker Ontology Model
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
Leveraged OODT software framework for
constructing ground data systems for earth
science missions
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Earth Science Distributed Process Mgmt
SeaWinds on
ADEOS II
(Launched
Dec 2002)
Used OODT Catalog and Archive Service
software
Constructed “workflows”
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Execution of “processors” based on a set of
rules
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Provided “lights out” operations
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Multiple Missions
User Interface (Process Monitoring & Control, Instrument Command ing, Data Verification)
Instrument
Commands
PreProce ssors
(PP)
En gi ne e rin g
An al ysis
(EA)
S cie n ce
Le ve l
Proce ssors
(LP)
S cie n ce
An al ysis
an d
Q u ality
Re portin g
(S A)
Spacecraft
& Ancillary
Files
Product Delivery (PM)
SeaWinds
QuikSCAT
Orbiting Carbon Observatory (OCO)
NP Sounder PEATE
SMAP
File Transfer (FX)
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Science
Products
Released
to
PO.DAAC
Data Management and Automatic Process Control (PM) using OODT
DJC-21
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
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Supporting Climate Research
Earth Observing System Data and Information System (EOSDIS)
serves NASA’s earth scientists data needs
Two major legacies are left
– Archiving of explosion in observational data in Distributed Active Archive
Centers (DAACs)
• Request-driven retrieval from archive is time consuming
– Adoption of Hierarchical Data Format (HDF) for data files
• Defined by and unique to each instrument but not necessarily consistent between
instruments
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What are the next steps to accelerating use of an ever increasing
observational data collection?
– What data are available?
– What is the information content?
– How should it be interpreted in climate modeling research?
National Aeronautics and
Space Administration
EOSDIS DAAC’s
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Earth Observing System Data and Information System
Distributed Active Archive Centers
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
EOSDIS DAAC’s
Earth Observing System Data and Information
System Distributed Active Archive Centers
Cumulative Volume of L2+ Products at All DAACs
4,000
3,500
Cumulative Volume (TB)
3,000
2,500
2,000
1,500
1,000
500
0
FY00
FY01
FY02
FY03
FY04
FY05
FY06
FY07
FY08
Fiscal Year
FY09
FY10
FY11
FY12
FY13
FY14
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Current Data System
• System serves static data products. User must find
move, and manipulate all data him/herself.
• User must change spatial and temporal resolutions
to match.
• User must understand instrument observation
strategies and subtleties to interpret.
National Aeronautics and
Space Administration
Climate Data eXchange (CDX)
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
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Develop an architecture that enables sharing of climate model output
and NASA observational data
– Develop an architectural model that evaluates trade space of model
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Provide extensive server-side computational services side
– Increase performance
– Subsetting, reformatting, re-gridding
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Deliver an “open source” toolkit
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Connect NASA and DOE
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Combining Instrument Data to enable Climate
Research: AIRS and MLS
Combining AIRS and MLS
requires:
– Rectifying horizontal,
vertical and temporal
mismatch
– Assessing and
correcting for the
instruments’ scenespecific error
characteristics (see left
diagram)
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
Climate Data Exchange
Key Questions to be Answered
Specific Tools
(H2O, CO2, …)
DJC-28
28
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
•
Summary
Software is critical to supporting collaborative research in science
– Virtual organizations
– Transparent access to data
– End-to-end environments
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Software architecture is critical to
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Reducing cost of building science data systems
Building virtual organizations
Constructing software product lines
Driving standards
Science is still learning how to best leverage technology in a
collaborative discovery environment, but significant progress is being
made!
DJC-29
National Aeronautics and
Space Administration
Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California
THANK YOU…
Dan Crichton
– Dan.Crichton@jpl.nasa.gov
– +1 818 354 9155
DJC-30