CSE

1
CSE e-Science

• Bertram Ludäscher
• Dept. of Computer Science
• Genome Center
• University of California, Davis
• ludaesch_at_ucdavis.edu

2
Computational Science Engineering

• Traditional view
• computational physics, computational chemistry,
  big simulations, Teraflops, Petabytes,
• Yes, but wait -- there is more
• Emergence of e-Science (UK, Europe),
  cyberinfrastructure (NSF), and NIH programs
• To illustrate this a bit more

3
Science has been changing lately

• THEN All science is either physics or stamp
  collecting.
• Ernest Rutherford, British chemist
  physicist (1871 - 1937)
• J. B. Birks "Rutherford at Manchester (1962)
• i.e., from few data, lots of thinking, to
• NOW Lots of Data Analysis
• ? Data-driven scientific discovery!

4
The Diversity Unity of Science
Natural Sciences

Earth Sciences
Life Sciences
Physical Sciences
Observations, Measurements, Models, Simulations,
Analyses, Hypotheses Understanding, Prediction,

in vivo, in vitro, in situ, in silico,
Data-, Knowledge-, Workflow- Management is
central to most of them!
compute-intensive
structurally semantics -intensive
data-intensive
metadata-intensive
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e-Science (UK) and Cyberinfrastructure (US)

• e-Science is about global collaboration in key
  areas of science and the next generation of
  computing infrastructure that will enable it."
• Sir John Taylor, Director Office of Science and
  Technology, UK
• "Cyberinfrastructure is the coordinated aggregate
  of software, hardware and other technologies, as
  well as human expertise, required to support
  current and future discoveries in science and
  engineering. The challenge of Cyberinfrastructure
  is to integrate relevant and often disparate
  resources to provide a useful, usable, and
  enabling framework for research and discovery
  characterized by broad access and 'end-to-end'
  coordination.
• Fran Berman, San Diego Supercomputer Center, UCSD

6
Towards 2020 Science Report (MSR)
http//research.microsoft.com/towards2020science

• new develoment at the intersection of computer
  science and the sciences a leap from the
  application of computing to support scientists to
  do science (i.e. computational science) to
  the integration of computer science concepts,
  tools and theorems into the very fabric of
  science. We believe this development
  represents the foundations of a new revolution in
  science
• we believe computer science is poised to become
  as fundamental to biology as mathematics has
  become to physics
• to understand cells and cellular systems
  requires viewing them as information processing
  systems, as evidenced by the fundamental
  similarity between molecular machines of the
  living cell and computational automata, and by
  the natural fit between computer process algebras
  and biological signalling and between
  computational logical circuits and regulatory
  systems in the cell
• We highlight that an immediate and important
  challenge is that of end-to-end scientific data
  management, from data acquisition and data
  integration, to data treatment, provenance and
  persistence.
• dramatic in its impact, will be the integration
  of new conceptual and technological tools from
  computer science into the sciences.

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Example Assembling the Tree of Life (AToL)
All organisms (alive or extinct) are part of one
large, genetically connected group Life on
Earth. Major subgroups Eubacteria, Archaea, and
Eukaryotesfurther divided into hierarchically
nested subgroups e.g., eukaryotes contains
plants, animals, fungi animals contains
sponges, cnidarians, Bilateria Bilateria
contains arthropods, molluscs, nematodes, etc.
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Inferring a phylogenetic tree from disparate data
Aligned DNA sequences
Maximum likelihood tree (DNA)
Discrete morphological data
Maximum parsimony tree
Integrate
Consensus Tree(s)
Maximum likelihood tree (continuous characters)
Continuous characters
NSF Collaborative Research (w/ UPenn) Core
Database Technologies to Enable the Integration
of AToL Information 462,000 (2006-2009)
Actors
Datasets
Datasets
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A Real-World Example (ChIP-chip workflow)
collaboration with UCD Genome Center
NSF/SEI(BIO)II A Collaborative Scientific
Workflow Environment for Accelerating
Genome-Scale Biological Research 600,139
(2006-2009)
10
DOE/SciDAC-2 SDM CPES Fusion Simulation
(Norbert Podhorszki UC Davis, Scott Klasky ORNL)
Monitor

• Plasma physics simulation on 2048 processors on
  Seaborg_at_NERSC (LBL)
• Gyrokinetic Toroidal Code (GTC) to study energy
  transport in fusion devices (plasma
  microturbulence)
• Generating 800GB of data (3000 files, 6000
  timesteps, 267MB/timestep), 30 hour simulation
  run
• Under workflow control
• Monitor (watch) simulation progress (via remote
  scripts)
• Transfer from NERSC to ORNL concurrently with the
  simulation run
• Convert each file to HDF5 file
• Archive files to 4GB chunks into HPSS

DOE/SciDAC-2 Scientific Data Management Center
(w/ LBL) 965,000 (2006-2011)
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Kepler and Sensor Networks

• These ones just in (new NSF CEOP projects)
• Management and Analysis of Environmental
  Observatory Data using the Kepler Scientific
  Workflow System, NCEAS, SDSC, UC Davis, OSU,
  CENS (UCLA), OPeNDAP
• standardize services for sensor networks, support
  multiple views, protocols
• COMET Coast-to-Mountain Environmental Transect,
  UC Davis, Bodega Marine Lab, Lake Tahoe Research
  Center
• study how environmental factors affect ecosystems
  along an elevation gradient from coastal
  California to the summit of the Sierra Nevada

CEOP--COMET Coast-to-Mountain Environmental
Transect 2,158,580 (2006-209)
CEOP--Management and Analysis of Environmental
Observatory Data Using the Kepler Scientific
Workflow System 290,000 (collaborative 2.9M)
(2006-2010)
CEOP/REAP
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Scientific Workflows Cyberinfrastructure
UPPER-WARE
Upperware
Upper Middleware
Middleware
Underware
NSF ITR (w/ SDSC) Science Environment for
Ecological Knowledge (SEEK) 2,485,683
(2002-2007)
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Consilience The Unity of Knowledge (E. O. Wilson)

• "Literally a jumping together of knowledge by the
  linking of facts and fact-based theory across
  disciplines to create a common groundwork for
  explanation." E.O.Wilson
• eScience, Cyberinfrastructure mechanisms to make
  progress
• Scientific Workflows crucial elements to get the
  most mileage out of CI to fuel eScience,
  accelerating knowledge discovery
• CSE needs computer scientists, domain scientists,
  hybrids (e.g. bioinformaticians,
  computational/simulation scientists)

14
Some Related Publications

• Semantic Type Annotation
• S Bowers, B Ludaescher. A Calculus for
  Propagating Semantic Annotations through
  Scientific Workflow Queries. ICDE Workshop on
  Query Languages and Query Processing (QLQP),
  LNCS, 2006.
• S Bowers, B Ludaescher. Towards Automatic
  Generation of Semantic Types in Scientific
  Workflows. International Workshop on Scalable
  Semantic Web Knowledge Base Systems (SSWS), WISE
  2005 Workshop Proceedings, LNCS, 2005.
• C Berkley, S Bowers, M Jones, B Ludaescher, M
  Schildhauer, J Tao. Incorporating Semantics in
  Scientific Workflow Authoring. SSDBM, 2005.
• B Ludaescher, K Lin, S Bowers, E Jaeger-Frank, B
  Brodaric, C Baru. Managing Scientific Data From
  Data Integration to Scientific Workflows. GSA
  Today, Special Issue on Geoinformatics, 2006.
• S Bowers, D Thau, R Williams, B Ludaescher. Data
  Procurement for Enabling Scientific Workflows On
  Exploring Inter-Ant Parasitism. VLDB Workshop on
  Semantic Web and Databases (SWDB), 2004.
• S Bowers, K Lin, B Ludaescher. On Integrating
  Scientific Resources through Semantic
  Registration. SSDBM, 2004.
• S Bowers, B Ludaescher. An Ontology-Drive
  Framework for Data Transformation in Scientific
  Workflows. International Workshop on Data
  Integration in the Life Sciences (DILS), LNCS,
  2004.
• S Bowers, B Ludaescher. Towards a Generic
  Framework for Semantic Registration of Scientific
  Data. International Semantic Web Conference
  Workshop on Semantic Web Technologies for
  Searching and Retrieving Scientific Data, 2003.
• Workflow Design and Modeling
• T McPhillips, S Bowers, B Ludaescher.
  Collection-Oriented Scientific Workflows for
  Integrating and Analyzing Biological Data.
  Workshop on Data Integration in the Life Sciences
  (DILS), LNCS, 2006.
• S Bowers, T McPhillips, B Ludaescher, S Cohen, SB
  Davidson. A Model for User-Oriented Data
  Provenance in Pipelined Scientific Workflows.
  International Provenance and Annotation Workshop
  (IPAW), LNCS, 2006.
• S Bowers, B Ludaescher, AHH Ngu, T Critchlow.
  Enabling Scientific Workflow Reuse through
  Structured Composition of Dataflow and
  Control-Flow. IEEE Workshop on Workflow and Data
  Flow for Scientific Applications (SciFlow), 2006.
• S Bowers, B Ludaescher. Actor-Oriented Design of
  Scientific Workflows. International Conference on
  Conceptual Modeling (ER), LNCS, 2005.
• T McPhillips, S Bowers. Pipelining Nested Data
  Collections in Scientific Workflows. SIGMOD
  Record, 2005.
• Kepler
• D Pennington, D Higgins, AT Peterson, M Jones, B
  Ludaescher, S Bowers. Ecological Niche Modeling
  using the Kepler Workflow System. Workflows for
  e-Science, Springer-Verlag, to appear.
• W Michener, J Beach, S Bowers, L Downey, M Jones,
  B Ludaescher, D Pennington, A Rajasekar, S
  Romanello, M Schildhauer, D Vieglais, J Zhang.
  SEEK Data Integration and Workflow Solutions for
  Ecology. Workshop on Data Integration in the Life
  Sciences (DILS), LNCS, 2005.
• S Romanello, W Michener, J Beach, M Jones, B
  Ludaescher, A Rajasekar, M Schildhauer, S Bowers,
  D Pennington. Creating and Providing Data
  Management Services for the Biological and
  Ecological Sciences Science Environment for
  Ecological Knowledge. SSDBM, 2005.

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Kepler Collaboration

• Open-source
• Builds on Ptolemy II from UC Berkeley
• Contributors from
• SEEK
• SciDAC SDM
• Ptolemy
• GEON
• ROADNet
• Resurgence
• AToL CIPRES, POD
• Goals
• Create powerful analytical tools that are useful
  across disciplines
• Ecology, Biology, Engineering, Geology, Physics,
  Chemistry, Astronomy,

Ptolemy II
Natural Diversity Discovery Project
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Databases Information Systems (DBIS)
DBIS.ucdavis.edu
DAKS.ucdavis.edu

• Profs. Michael Gertz, Bertram Ludaescher
• Drs. Shawn Bowers, Timothy McPhillips, Norbert
  Podhorszki
• 12 graduate students