Scientific Workflows: More eScience Mileage from Cyberinfrastructure

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Scientific Workflows More e-Science Mileage
from Cyberinfrastructure

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

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SUMMARY (first things first, but
not necessarily in that order)

• Scientific workflows are CI upperware for
  eScience
• Scientific workflows are universal (everyware)
  and there are many interesting technical
  challenges
• execution models, semantic extensions,
  provenance,
• modeling and design!
• Workflow Thinking!
• Collection-Oriented Modeling Design (COMAD) is
  good for you! (YMMV)
• Introduction
• on workflow legacy
• i.e. data provenance issues.

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Scientific workflows are CI upperware, i.e.
the scientists way to harness
cyberinfrastructure

• Domain Scientists View
• Q When is CI (middleware, underware) good?
• A When I cant see it!
• Q When is a scientific workflow tool (CI
  upperware) good?
• A When I can get more, new, faster, better
  science done!
• Workflow Engineers View
• How can I (help the scientist) design implement
  the desired wfs?
• How does wf make my life easier? Beyond Perl,
  Python? in there beyond just using Perl? Python?
  
• Choice of platforms, standards reuse of existing
  tools, semantic extensions, scheduling on the
  Grid?
• How do I make all of this robust, fault-tolerant,
  etc.
• Computer Scientists View
• workflow language design, static analysis,
  optimization, theoretical limits what can and
  cant be done

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Science Environment for Ecological Knowledge
(SEEK)
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Scientific Workflow

• Capture how a scientist works with data and
  analytical tools
• data access, transformation, analysis,
  visualization
• possible worldview dataflow-oriented (cf.
  signal-processing)
• Scientific workflow (wf) benefits (compare w/
  script-based approaches)
• wf automation
• wf component reuse
• wf design, documentation
• wf archival, sharing
• built-in concurrency
• (task-, pipeline-parallelism)
• built-in provenance support
• distributed execution
• (Grid) support

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Ex SEEK Ecological Niche Modeling Pipeline

• Scientific Workflow paradigm
• Reusable components (actors) a scientists
  verbs/actions
• Top-level workflows conceptual representation
  of the science process, sentences in the
  scientists language
• Sub-workflows increasing levels of detail
• Separation of concerns
• actors what to do
• parameters configurable behavior
• channels dataflow, pipeline composition
• directors fix execution model, scheduling
• semantic types smart discovery, linking

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.
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Simple Kepler workflow using R (a statistics
package)
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Plumbing with Style (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

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(No Transcript)
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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
CEOP/Kepler
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Workflow Thinking (cf. Computational
Thinking)

• How should we think about scientific workflows?
• From What scientists do to produce scientific
  papers
• to its just a (Turing) program
• Is that helpful?
• Who are you? What are you trying to do?
• (Domain) Scientist ? Workflow Engineer ? Computer
  Scientist

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Our Starting Point Actor-Oriented Modeling

• Ports
• each actor has a set of input and output ports
• denote the actors signature
• produce/consume data (a.k.a. tokens)
• parameters are special static ports

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Actor-Oriented Modeling

• Dataflow Connections
• unidirectional actor communication channels
• connect output ports with input ports
• for composing analysis pipelines

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Actor-Oriented Modeling

• Sub-workflows / Composite Actors
• composite actors wrap sub-workflows
• like actors, have signatures (i/o ports of
  sub-workflow)
• hierarchical workflows (arbitrary nesting levels)

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Actor-Oriented Modeling

• Directors
• define the execution semantics of workflow graphs
• executes workflow graph (some schedule)
• sub-workflows may have different directors
• promotes reusability

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Models of Computation (A Wf Engineers Issue)

• Directors separate the concerns of orchestration
  and scheduling from conceptual design
• Synchronous Dataflow (SDF)
• Statically analyzable schedule, no deadlocks,
  fixed buffer requirements executable as a single
  thread by the director.
• Process Networks (PN)
• Generalizes SDF. Actors execute as separate
  threads/processes, with queues of unbounded size
  (Kahn/MacQueen networks).
• Directed Acyclic Graph (DAG)
• Special case of SDF. No loops, no pipelining.
• Continuous Time (CT)
• Connections represent the value of a continuous
  time signal at some point in time ... Often used
  to model physical processes.
• Discrete Event (DE)
• Actors communicate through a queue of events in
  time. Used for instantaneous reactions in
  physical systems.

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Everything is a service / actor
( yeah right)
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Scientific Workflow Design Challenges
And thats why our scientific workflows are
much easier to develop, understand and maintain!
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Shimology Part 1 Structure Semantics

• Components and their i/o ports typically have
• Explicit structural type
• e.g., int, float, string, array.... of
  double,
• Implicit semantic type
• Not sure whether the stream of values from a port
  represents rainfall values or body size values

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Semantic Annotation

• Label data with semantic types
• Label inputs and outputs of analytical components
  with semantic types (and overall component
  function)
• Grounded at level of measurement and data,
  avoiding some pitfalls of upper ontologies

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Hybrid Types Semantic Structural Typing
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.
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Semantic Type Annotation in Kepler

• Component input and output port annotation
• a port can be annotated with multiple concepts
  from multiple ontologies
• Annotations are stored with the actor metadata

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Component Annotation and Indexing

• Component Annotations
• New components can be annotated and indexed into
  the actor library
• (specializing generic actors)
• Existing components can be revised, annotated,
  and indexed (hiding previous versions)

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Smart Discovery

• Find a component (here an actor) in different
  locations (categories)
• based on the semantic annotation of the
  component (or its ports)

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Smart Linking (Workflow Design)

• Statically perform semantic and structural type
  checking

• Navigate errors and warnings within the workflow
• !! Search for and insert adapters (aka shims) to
  fix (structural and semantic) mismatches

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Smart Linking (addressing Shimology
Type 1)
Source Bowers-Ludaescher, DILS04
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Scientific Workflow Design More Challenges
And thats why our scientific workflows are
much easier to develop, understand and maintain!
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Behold the Beauty of Scientific Workflow Design
Author Kristian Stevens, UC Davis
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Shimology Part 2 the ugly truth inside
Author Kristian Stevens, UC Davis
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And now a Real Example! (ChIP-chip workflow)

• Fear Not!
• You are in the good hands of the DAKS (and
  Farnham) Labs _at_ UC Davis Genome Center
• Higher-order functions (map, fold, zipWith, )
  shall oil your gears , e.g.,
• map f x1,,xn ? f(x1), , f(xn)
• MAP f1, , fk (x) ? f1(x), , fk(x)
• Your analysis pipeline shall be automated,
• your data lineage shall be recorded,
• and your provenance explained!

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But how do we get from messy to neat reusable
designs?
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The Answer (YMMV)

• Collection-Oriented Modeling Design (COMAD)
• embrace the assembly line metaphor fully
• ? cf. Flow-based Programming (J. Morrison)
• data tagged nested collections
• e.g. represented as flattened, pipelined
• (XML) token streams

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How does COMAD work?

• Some COMAD principles
• data tagged, flattened, nested collections
  (token streams)
• data tokens
• metadata tokens
• inherited downwards into (sub)collections
• define an actors read scope via an (X)Path-like
  expression
• default actor behavior
• not mine?
• ? dont do anything just pass the buck!
• stuff within my scope? ?
• add-only to it (default)
• consume scope write-out result
• (but remember the bypass!)
• iteration scope is a query involving group-by and
  further refines the granularity/subtrees that
  constitute the tokens consumed by an actor firing
  
• has aspects of implicit iteration (a la Taverna)
• default iteration level to fix signature
  mismatches
• but also
• granularity/grouping is definable
• works on anything (assuming scope is matched
  correctly)

• T McPhillips, S Bowers. Pipelining Nested Data
  Collections in Scientific Workflows. SIGMOD
  Record, 2005.
• 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), 2006

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COMAD What we gained

• from fragile, messy workflow designs
• to more reusable actors
• just change the read/iteration scope parameters!
• sometimes not even that is needed (working on
  that )
• and cleaner workflow design (The A-B-C method
  of wf design!)
• Crux keep the nesting structure of data (pass
  through, add-only)
• and let it drive the (semi-)implicit iteration
  (aka structural recursion )

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Workflow Thinking Modeling Design Paradigms

• Vanilla Process Network
• Functional Programming Dataflow Network
• XML Transformation Network
• Collection-oriented Modeling Design framework
  (COMAD)

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A Scientific Publication (the final
provenance frontier )
Title (Statement, Theorem)
Abstract (1st-Level- Expansion)
Main Text (2nd-Level Expansion)
Nature 443, 167-172(14 September 2006)
doi10.1038/nature05113 Received 27 June 2006
Accepted 25 July 2006 Published online 16 August
2006
some metadata
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More Evidence
data reference
type of evidence
tool reference
trust me on this one

• provenance/lineage show the history and evidence
• related to proof trees
• unlike w/ scripts, SWF system can keep track of
  what happened
• In the future deposit your data workflows in a
  repository

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Pipelined workflow for inferring phylogenetic
trees
Author Tim McPhillips, UC Davis
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Scientific Provenance Questions we can ask

• What DNA sequences were input to the workflow
  (this run)?
• What phylogenetic trees were output by the
  workflow?
• What phylogenetic trees were created
  (intermediate or final) by the workflow?
• What actor (recall a verb in the scientists
  language) created this phylogenetic tree?
• What sequences input to the workflow does this
  consensus tree depend on?
• What input sequences were not used to derive any
  output consensus trees?
• What was the sequence alignment (key intermediate
  data) used in the process of inferring this tree?
• Which actors were involved in creating this tree?

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Provenance in the COMAD Framework
Without Provenance

• COMAD in a nutshell
• look at an unstructured XML token stream
• through the scope expression lenses
• thus seeing nested, tagged data collections
• pass the buck on stuff thats not yours
• add computed data (workflow actors do that)
• add provenance metadata (COMAD framework does
  that)

With Provenance
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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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Coming up Kepler Actor Repository
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Workflow and component repositories myExperiment
is your library, or Our
Workflow Repository!

• Taverna Repository Kepler
  Repository
• Workflow system speciation,
• looking for a new symbiotic relation --- need
  enzymes!
• (yes, thats for the rest of us )

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Acknowledgements, QA

• Data and Knowledge Systems Lab (DAKS) _at_ UC Davis
• Dr. Shawn Bowers, Dr. Timothy McPhillips, Dr.
  Norbert Podhorszki
• Dave Thau, Daniel Zinn, Alex Chen
• Many Kepler collaborators
• Ilkay Altintas (SDSC), Matt Jones (UCSB), Arie
  Shoshani (LBL), Terence Critchlow (LLNL), Mladen
  Vouk (NCSU),

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Further Reading

• Special issues
• General SIGMOD Record, Sept. 05
• SWF Provenance CCPE , on 1st Provenance
  Challenge WS
• Oldies but goldies
• The Emperors Old Clothes, Tony Hoare, ACM Turing
  Award Lecture
• The Evolution of Language (a slide by Peter
  Buneman on Phil Wadlers website I think)
• Additional References
• next page

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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 Ludaesher. 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), 2006, to appear.
• 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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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,
compute-intensive
structurally semantics -intensive
data-intensive
metadata-intensive