# Presentation: 248967

## Looking to the Future

- Computation Institute
- Argonne National Lab & University of Chicago
- http://ianfoster.typepad.com

## An American Philosopher Speaks on the Value of KAREN

-  “80 percent of success is showing up”

- Woody Allen

## J.C.R. Licklider Reflecting in 1960 on Where His Time Went

-    About 85 per cent of my “thinking” time was spent getting into a position to think, to make a decision, to learn something I needed to know

## For Example ...

-   “At one point, it was necessary to compare six experimental determinations of a function relating speech-intelligibilityto speech-to-noise ratio. No two experimenters had used the same definition or measure of speech-to-noise ratio. Several hours of calculating were required to get the data into comparable form. When they were in comparable form, it took only a few seconds to determine what I needed to know.”

## Man-Computer Symbiosis

-  “... will involve very close coupling between the human and the electronic members of the partnership ... Preliminary analyses indicate that the symbiotic partnership will perform intellectual operations much more effectively than man alone can perform them.”

## Licklider’s Vision

- He imagined
- Low-cost personal computers
- Organized, easily searchable data stores
- Intuitive user interfaces
- Computer aided reasoning
- Interactive numerical simulation
- He didn’t mention (at least in CMS paper)
- Standard representations of knowledge
- Collaborative problem solving among people

## Fast Forward to 2007

-  “Compare six experimental determinations of a function relating speech-intelligibilityto speech-to-noise ratio”

## Emergence of New Problem Solving Methodologies

- <0

- 1700

- 1950

- 1990

- eScience: When brute force doesn’t work anymore (Szalay)

**Notes:**
Aristotle, Leibniz, von Neuman, Gray

Theory=math
Simulation = math + CS
Data = bits and algorithms, i.e., CS.

Different disciplines advanced to different stages: e.g., parking still empirical (-:

## Functional Magnetic Resonance Imaging (fMRI)

## The fMRI Data Center(www.fmridc.org)

- The fMRI Data Center(www.fmridc.org)

- A typical study comprises 	3 groups, 	20 subjects/group, 	5 runs/subject, 	300 volumes/run  90,000 volumes, 60 GB raw  1.2 million files processed
- 100s of such studies in total

**Notes:**
What’s fmridc, explain run
20 step workflow w/ thousands of images

## Functional MRI Analysis

**Notes:**
Data sharing, publishing, reproducing results, making new discovery

## A Simple Analysis

- A Simple Analysis

## First Generation Grids:On-Demand/Batch Computing

- First Generation Grids:On-Demand/Batch Computing

-   Focus on aggregation of many resources for massively (data-)parallel applications

- EGEE

## Dynamic Allocation for Dynamic Workloads 

- Dynamic Allocation for Dynamic Workloads

## Many Users Analyze fMRI Datain Many Different Ways

- Wide range of analyses
- Testing, interactive analysis, production runs
- Data mining
- Parameter studies

**Notes:**
Data sharing, publishing, reproducing results, making new discovery

## Second Generation Grids:Service-Oriented Science

- Empower many more users by enabling on-demand access to **services**
- Grids become an enabling technology for **service oriented science** (or business)
- Grid infrastructures host services
- Grid technologies used to build services

- “Service-Oriented Science”, *Science*, 2005

*Science*
*Gateways*

## “Web 2.0”

- Software as services
- Data- & computation-richnetwork services
- Services as platforms
- Easy composition of services to create new capabilities (“mashups”)—that themselves may be made accessible as new services
- Enabled by massive infrastructure buildout
- Google projected to spend $1.5B on computers, networks, and real estate in 2006
- Many others are spending substantially
- Paid for by advertising

- Declan
- Butler,
- Nature

**Notes:**
Services
Composition
Infrastructure

## Service-Oriented Science:E.g., Virtual Observatories

**Data Archives**

- Gateway

- Figure: S. G. Djorgovski

## Service-Oriented Science

- People **create** services (data or functions) ...
- which I **discover** (& decide whether to use) ...
- & **compose** to create a new function ...
- & then **publish** as a new service.
-  *I find “someone else” to ****host**** services, **so I don’t have to become an expert in operating* *services & computers!*
-  *I hope that this “someone else” can ****manage**** security, reliability, scalability, ...*

- !

- !

- “Service-Oriented Science”, *Science*, 2005

## Service-Oriented Science& Cancer Biology

*caBIG: sharing of infrastructure, applications, and data. *

**Data**
**Integration!**

- Service-Oriented Science& Cancer Biology

**Notes:**
I would mention data integration here: integration of different data types: tissue, (pre)clinical, genomics, proteomics....

## Cancer Bioinformatics Grid

**NCICB**

**Research**
**Center**

***Grid-Enabled Client***

**Research**
**Center**

**Tool 1**

**Tool 2**

**Tool 3**

**Tool 4**

***Grid Data Service***

***Analytical Service***

***Grid Portal***

## MEDICUS: Management of DICOM Images

- Imaging Modality

- Dicom Grid Interface Service

- (

- DGIS

- )

- Cache Image

- Storage

- 1

- .

- Grid PACS Meta Catalog

- (

- DICOM Image Attributes

- )

- OGCE

- -

- DAI

- .

**Hospital Domain**

**Grid Node**

**Grid Node**

- Imaging Technologist

- Stephan Erberich, Manasee Bhandekar, Ann Chervenak, et al.

- Globus

## Children’s Oncology Grid:A MEDICUS Deployment

- Globus

## MEDICUS Under the Covers

- DICOM images
- Send 		(publish)
- Query/Retrieve 	(discover)
- Grid Archive
- Fault tolerant
- Bandwidth
- Security
- Authentication
- Authorization
- Cryptography
- Access
- Web portal
- Applications
- Computing
- Data Mining

_**Globus Toolkit Release 4**_

- Globus

## Collaborative Analysis

- Collaborative Analysis

## The Virtual Data Concept

- Capture information about relationships among
- Data (varying locations and representations)
- Programs (& inputs, outputs, constraints)
- Computations (& execution environments)
- Apply this information to:
- Discovery of data and programs
- Computation management
- Provenance
- Planning and scheduling
- Performance optimization

- A Virtual Data System for Representing, Querying & Automating Data Derivation [SSDBM02]

**Notes:**
Data, programs, computations all important community resources

## Provenance Model

- Temporal aspect
**Prospective** provenance
- Recipes for how to produce data
- Metadata annotations about procedures and data
**Retrospective** provenance
- Invocation records of run time environments and resources used: site, host, executable, execution time, file stats ...
- Dimensional aspect
**Virtual data** relationships
**Derivation** lineage
**Metadata** annotations

- Applying the Virtual Data Provenance Model [IPAW06]

## Query Context: fMRI Analysis

- First Provenance Challenge, http://twiki.ipaw.info/

**Notes:**
Datasets in parallelogram, procedures in ovals.
How an image should be spatially adjusted to match the reference brain, output warp parameter set.
The 3D image is sliced in x, y, z dimensions to give a 2D atlas in each dimension. 

## Query Examples

- Query by procedure signature
- Show procedures that have inputs of type *subjectImage* and output types of *warp*
- Query by actual arguments
- Show *align_warp* calls (including all arguments), with argument *model*=*rigid*
- Query by annotation
- List anonymized subject images for young subjects:
- Find datasets of type *subjectImage* , annotated with *privacy=anonymized* and *subjectType=young*
- Basic lineage graph queries
- Find all datasets derived from dataset ‘5a’
- Graph pattern matching
- Show me all output datasets of *softmean* calls that were aligned with *model=affine*
- Multi-dimensional query

- “Grids are not just communities of     computers, but communities of   researchers, of people.”
- 	— Peter Arzberger

## PRAGMA

- New communities:

- Globus Downloads Last Month

## Grids are Communities of People as well as Computers

- Based on (technology-mediated) trust
- Common goals
- Processes and policies
- Reward systems
- That share resources
- Computers
- Data
- Sensor networks
- Services
- Supported by software, standards, infrastructure

## Security Services for Virtual Organization Policy

- Security Services for Virtual Organization Policy

- Attribute Authority (ATA)
- Issue signed attribute assertions (incl. identity, delegation & mapping)
- Authorization Authority (AZA)
- Decisions based on assertions & policy
- Use with message- or transport-level security

*VO A*
*Service*

*VO*
*ATA*

*VO*
*AZA*

*Mapping*
*ATA*

*VO B*
*Service*

*VO**User A*

*Delegation Assertion*
*User B can use Service A*

*VO-A Attr *
*       **VO-B Attr*

*VO**User B*

*Resource Admin*
*Attribute*

*VO Member*
*Attribute*

*VO Member Attribute*

- Bennett Berthenthal et al., www.sidgrid.org

- Social Informatics Data Grid

## Collaborative Tagging and Other Web 2.0 Things

## Automating Science Protocols

-    **Protocol**: “a predefined written procedural method in the design and implementation of experiments. This should establish standards that can be adequately assessed by peer review and provide for successful replication of results by others in the field.”
- 				(Wikipedia)

## Robot Scientist(University of Wales)

- Robot Scientist(University of Wales)

## Microfluidic Bubble Logic(Prakash and Gershenfeld)

## Man-Computer Symbiosis:50 Years On

- Service oriented science
- Provenance
- Virtual organizations
- Automating science protocols

## Concluding Thoughts

- Networks are necessary but not sufficient
- There is only global software
- We build communities by doing experiments
- Persistent infrastructure is needed
- Persistent workers are needed
- Showing up
- Team New Zealand?
- Where are the students?

- Travel is fatal to prejudice, bigotry and narrow-mindedness